Searching for decision : 400 results found | RSS Feed for this search

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Readme file for Structured Systems Analysis

Description

This readme file contains details of links to all the Readme file for Structured Systems Analysis module's material held on Jorum and information about the module as well.

Subjects

ukoer | current logical data flow diagram example | current logical data flow diagram exercise | current logical data flow diagram teaching guide | current logical data flow diagram video lecture | current logical data flow diagram | current logical data flow diagrams example | current logical data flow diagrams exercise | current logical data flow diagrams teaching guide | current logical data flow diagrams video lecture | current logical data flow diagrams | current logical dfd example | current logical dfd exercise | current logical dfd teaching guide | current logical dfd video lecture | current logical dfd | current logical dfds example | current logical dfds exercise | current logical dfds lecture | current logical dfds teaching guide | data dictionary example | data dictionary exercise | data dictionary lecture | data dictionary reading material | data dictionary teaching guide | data dictionary video lecture | data dictionary | data example | data exercise | data flow diagram example | data flow diagram exercise | data flow diagram reading material | data flow diagram teaching guide | data flow diagram video lecture | data flow diagram | data flow diagrams example | data flow diagrams exercise | data flow diagrams reading material | data flow diagrams teaching guide | data flow diagrams video lecture | data flow diagrams | data reading material | data teaching guide | data video lecture | data | decision table and tree example | decision table and tree exercise | decision table and tree reading material | decision table and tree teaching guide | decision table and tree video lecture | decision table and tree | decision table example | decision table exercise | decision table reading material | decision table teaching guide | decision table video lecture | decision table | decision tables and trees example | decision tables and trees lecture | decision tables and trees reading material | decision tables example | decision tables exercise | decision tables reading material | decision tables teaching guide | decision tables video lecture | decision tables | decision tree example | decision tree exercise | decision tree reading material | decision tree teaching guide | decision tree video lecture | decision tree | decision trees and decision tables exercise | decision trees and decision tables teaching guide | decision trees and decision tables video lecture | decision trees and decision tables | decision trees example | decision trees exercise | decision trees reading material | decision trees teaching guide | decision trees video lecture | decision trees | dfd example | dfd exercise | dfd reading material | dfd teaching guide | dfd video lecture | dfd | dfds example | dfds exercise | dfds reading material | dfds teaching guide | dfds video lecture | dfds | exploding data flow diagrams example | exploding data flow diagrams exercise | exploding data flow diagrams reading material | exploding data flow diagrams teaching guide | exploding data flow diagrams | exploding dfd example | exploding dfd exercise | exploding dfd reading material | exploding dfd teaching guide | exploding dfd | logical data flow diagram example | logical data flow diagram exercise | logical data flow diagram reading material | logical data flow diagram teaching guide | logical data flow diagram video lecture | logical data flow diagram | logical data flow diagrams example | logical data flow diagrams exercise | logical data flow diagrams teaching guide | logical data flow diagrams video lecture | logical data flow diagrams | logical dfd example | logical dfd exercise | logical dfd reading material | logical dfd teaching guide | logical dfd video lecture | logical dfd | logical dfds example | logical dfds exercise | logical dfds reading material | logical dfds teaching guide | logical dfds video lecture | logical dfds | project management practical | project management reading material | project management task guide | project management teaching guide | project management | quality management | quality managment reading material | quality managment task guide | required logical data flow diagram example | required logical data flow diagram exercise | required logical data flow diagram reading material | required logical data flow diagram teaching guide | required logical data flow diagram video lecture | required logical data flow diagram | required logical data flow diagrams example | required logical data flow diagrams exercise | required logical data flow diagrams reading material | required logical data flow diagrams teaching guide | required logical data flow diagrams video lecture | required logical data flow diagrams | required logical dfd example | required logical dfd exercise | required logical dfd reading material | required logical dfd teaching guide | required logical dfd video lecture | required logical dfd | required logical dfds example | required logical dfds exercise | required logical dfds lecture | required logical dfds reading material | required logical dfds teaching guide | structured chart example | structured chart exercise | structured chart reading material | structured chart teaching guide | structured chart video lecture | structured chart | structured charts example | structured charts exercise | structured charts lecture | structured charts reading material | structured charts teaching guide | structured charts video lecture | structured charts | structured english example | structured english exercise | structured english lecture | structured english teaching guide | structured english video lecture | structured english | structured system analysis example | structured system analysis exercise | structured system analysis lecture | structured system analysis practical | structured system analysis reading material | structured system analysis task guide | structured system analysis teaching guide | structured system analysis video lecture | structured system analysis | structured systems analysis example | structured systems analysis exercise | structured systems analysis lecture | structured systems analysis practical | structured systems analysis reading material | structured systems analysis task guide | structured systems analysis teaching guide | structured systems analysis video lecture | structured systems analysis | structured walkthroughs reading material | system analysis example | system analysis exercise | system analysis lecture | system analysis practical | system analysis reading material | system analysis task guide | system analysis teaching guide | system analysis video lecture | system analysis | systems analysis example | systems analysis exercise | systems analysis lecture | systems analysis practical | systems analysis reading material | systems analysis task guide | systems analysis teaching guide | systems analysis video lecture | systems analysis | techniques in methods lecture | techniques in methods teaching guide | techniques in methods | uml | univeral modelling language lecture | univeral modelling language | universal modeling language lecture | universal modeling language | current logical dfds video lecture | current logical dfds | exploding dfds example | exploding dfds exercise | exploding dfds reading material | exploding dfds teaching guide | exploding dfds | levelling dfds example | levelling dfds exercise | levelling dfds reading material | levelling dfds teaching guide | levelling dfds | required logical dfds video lecture | required logical dfds | uml lecture | Computer science | I100

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Site sourced from

http://dspace.jorum.ac.uk/oai/request?verb=ListRecords&metadataPrefix=oai_dc

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

Readme file for Introduction to OO Programming in Java

Description

This readme file contains details of links to all the Introduction to OO Programming in Java module's material held on Jorum and information about the module as well.

Subjects

ukoer | programming task guide | programming lecture | programming reading material | software design reading material | classes guide | libraries lecture | classes reading material | classes visual aid | software objects guide | graphics reading material | attributes reading material | attributes visual guide | naming conventions reading material | code reading material | java keywords reading material | variables visual guide | arithmetic reading material | java assignment | making decisions task guide | making decisions lecture | making decisions reading material | boolean expressions visual guide | repetition reading material | while loops visual guide | methods reading material | methods practical | access modifiers reading material | objects reading material | classes assignment | classes practical | child classes task guide | inheritance task guide | extending classes lecture | inheritance reading material | inheritance visual guide | inheritance practical | graphics task guide | awt reading material | graphics visual aid | awt class library reading material | event-driven programming reading material | scrollbars reading material | reflective practice visual guide | mobile phone task guide | mobile phone lecture | fixed repitition reading material | fixed repitition visual guide | mobile phone library reading material | mobile phone reading material | arrays task guide | arrays lecture | arrays reading material | arrays visual guide | creating software objects reading material | software objects visual guide | java practical | generic array list task guide | overriding methods reading material | menu and switch task guide | multi-way decisions reading material | multi-way decisions visual guide | searching task guide | searching lecture | searching reading material | software quality task guide | software quality lecture | software quality reading material | programming assignment | applet reading material | classes visual guide | object-oriented programming | object-oriented | programming | java | problem solving | java program | software design | programming languages | computers | class task guide | class reading material | class assignment | class practical | java classes | variables | attributes | arithmetic | java class | classes and arithmetic | classes | class | decisions | boolean expression | boolean expressions | repetition | methods | aggregate classes | access modifier | access modifiers | child classes | inheritance | child class | graphics | awt class library | fixed repetition | for loop | for loops | array | arrays | iteration | software object | definite iteration | generic lists | generic array list | cast | casting | overriding method | overriding methods | generic list | menu-driven program | menu-driven programs | multi-way decisions | menu and switch | search | searching | software quality | testing | software quality and testing | assessment | computers task guide | programming languages task guide | software design task guide | java program task guide | problem-solving task guide | problem solving task guide | object-oriented programming task guide | java task guide | object-oriented task guide | object oriented task guide | computers lecture | programming languages lecture | software design lecture | java program lecture | problem solving lecture | object-oriented programming lecture | java lecture | object oriented programming lecture | object-oriented lecture | computers reading material | programming languages reading material | java program reading material | problem solving reading material | object-oriented programming reading material | java reading material | object-oriented reading material | object oriented reading material | java classes task guide | variables task guide | attributes task guide | arithmetic task guide | java class task guide | classes and arithmetic task guide | classes task guide | java classes lecture | variables lecture | attributes lecture | arithmetic lecture | java class lecture | classes and arithmetic lecture | classes lecture | class lecture | java classes reading material | variables reading material | java class reading material | classes and arithmetic reading material | java classes visual aid | variables visual aid | attributes visual aid | arithmetic visual aid | java class visual aid | classes and arithmetic visual aid | class visual aid | java visual aid | object-oriented programming visual aid | programming visual aid | object-oriented visual aid | decisions task guide | boolean expression task guide | boolean expressions task guide | repetition task guide | methods task guide | decisions lecture | boolean expression lecture | boolean expressions lecture | repetition lecture | methods lecture | decisions reading material | boolean expression reading material | boolean expressions reading material | decisions visual aid | boolean expression visual aid | boolean expressions visual aid | repetition visual aid | methods visual aid | decisions practical | boolean expression practical | boolean expressions practical | repetition practical | programming practical | object oriented programming practical | object-oriented programming practical | object-oriented practical | object oriented practical | aggregate classes task guide | access modifier task guide | access modifiers task guide | aggregate classes lecture | access modifier lecture | access modifiers lecture | aggregate classes reading material | access modifier reading material | aggregate classes assignment | java classes assignment | access modifier assignment | access modifiers assignment | object oriented programming assignment | object-oriented programming assignment | object-oriented assignment | object oriented assignment | child class task guide | child classes lecture | inheritance lecture | child class lecture | child classes reading material | child class reading material | child classes visual aid | inheritance visual aid | child class visual aid | awt class library task guide | graphics lecture | awt class library lecture | awt class library visual aid | graphics assignment | awt class library assignment | fixed repetition task guide | fixed repetition lecture | fixed repetition visual aid | fixed repetition reading material | for loop task guide | for loops task guide | array task guide | iteration task guide | software object task guide | definite iteration task guide | for loop lecture | for loops lecture | array lecture | iteration lecture | software object lecture | definite iteration lecture | for loop reading material | for loops reading material | array reading material | iteration reading material | software object reading material | definite iteration reading material | for loop visual aid | for loops visual aid | array visual aid | arrays visual aid | iteration visual aid | software object visual aid | definite iteration visual aid | generic lists task guide | cast task guide | casting task guide | overriding method task guide | overriding methods task guide | generic list task guide | generic lists lecture | generic array list lecture | cast lecture | casting lecture | overriding method lecture | overriding methods lecture | generic list lecture | generic lists reading material | generic array list reading material | cast reading material | casting reading material | overriding method reading material | generic list reading material | menu-driven program task guide | menu-driven programs task guide | multi-way decisions task guide | menu-driven program lecture | menu-driven programs lecture | multi-way decisions lecture | menu and switch lecture | menu-driven program reading material | menu-driven programs reading material | menu and switch reading material | menu-driven program visual aid | menu-driven programs visual aid | multi-way decisions visual aid | menu and switch visual aid | search task guide | search lecture | search reading material | testing task guide | software quality and testing task guide | testing lecture | software quality and testing lecture | testing reading material | software quality and testing reading material | assessment reading material | assessment assignment | fixed repetition practical | jcreator guide | g622 | oo | oop | oo programming | awt | oo programming task guide | oop task guide | oo task guide | g622 task guide | oo programming lecture | oop lecture | oo lecture | g622 lecture | oo programming reading material | oop reading material | oo reading material | g622 reading material | g622 visual aid | oop visual aid | oo visual aid | oo programming visual aid | g622 practical | oo practical | oo programming practical | oop practical | g622 assignment | oo assignment | oop assignment | oo programming assignment | awt task guide | awt lecture | awt visual aid | awt assignment | Computer science | I100

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Site sourced from

http://dspace.jorum.ac.uk/oai/request?verb=ListRecords&metadataPrefix=oai_dc

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

Structured Systems Analysis - Decision Tables and Trees

Description

This video lecture forms part of the "Decision Tables and Trees" topic in the Structured Systems Analysis module.

Subjects

decision tables and trees lecture | decision trees | decision tables | decision trees and decision tables | systems analysis | structured systems analysis | system analysis | structured system analysis | decision table | decision tree | decision table and tree | decision trees video lecture | decision tables video lecture | decision trees and decision tables video lecture | systems analysis video lecture | structured systems analysis video lecture | system analysis video lecture | structured system analysis video lecture | decision table video lecture | decision tree video lecture | decision table and tree video lecture | Computer science | I100

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Site sourced from

http://dspace.jorum.ac.uk/oai/request?verb=ListRecords&metadataPrefix=oai_dc

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

Structured Systems Analysis - Decision Tables and Trees

Description

This teaching guide forms part of the "Decision Tables and Trees" topic in the Structured Systems Analysis module.

Subjects

ukoer | decision trees | decision tables | decision trees and decision tables | systems analysis | structured systems analysis | system analysis | structured system analysis | decision table | decision tree | decision table and tree | decision trees teaching guide | decision tables teaching guide | decision trees and decision tables teaching guide | systems analysis teaching guide | structured systems analysis teaching guide | system analysis teaching guide | structured system analysis teaching guide | decision table teaching guide | decision tree teaching guide | decision table and tree teaching guide | Computer science | I100

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Site sourced from

http://dspace.jorum.ac.uk/oai/request?verb=ListRecords&metadataPrefix=oai_dc

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

Structured Systems Analysis - Decision Tables and Trees

Description

This exercise forms part of the "Decision Tables and Trees" topic in the Structured Systems Analysis module.

Subjects

ukoer | decision trees | decision tables | decision trees and decision tables | systems analysis | structured systems analysis | system analysis | structured system analysis | decision table | decision tree | decision table and tree | decision trees exercise | decision tables exercise | decision trees and decision tables exercise | systems analysis exercise | structured systems analysis exercise | system analysis exercise | structured system analysis exercise | decision table exercise | decision tree exercise | decision table and tree exercise | Computer science | I100

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Site sourced from

http://dspace.jorum.ac.uk/oai/request?verb=ListRecords&metadataPrefix=oai_dc

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

Structured Systems Analysis - Decision Tables and Trees

Description

This exercise forms part of the "Decision Tables and Trees" topic in the Structured Systems Analysis module.

Subjects

ukoer | decision trees | decision tables | decision trees and decision tables | systems analysis | structured systems analysis | system analysis | structured system analysis | decision table | decision tree | decision table and tree | decision trees exercise | decision tables exercise | decision trees and decision tables exercise | systems analysis exercise | structured systems analysis exercise | system analysis exercise | structured system analysis exercise | decision table exercise | decision tree exercise | decision table and tree exercise | Computer science | I100

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Site sourced from

http://dspace.jorum.ac.uk/oai/request?verb=ListRecords&metadataPrefix=oai_dc

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

Structured Systems Analysis - Decision Tables and Trees

Description

This example forms part of the "Decision Tables and Trees" topic in the Structured Systems Analysis module.

Subjects

ukoer | decision tables and trees example | decision trees | decision tables | decision trees and decision tables | systems analysis | structured systems analysis | system analysis | structured system analysis | decision table | decision tree | decision table and tree | decision trees example | decision tables example | systems analysis example | structured systems analysis example | system analysis example | structured system analysis example | decision table example | decision tree example | decision table and tree example | Computer science | I100

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Site sourced from

http://dspace.jorum.ac.uk/oai/request?verb=ListRecords&metadataPrefix=oai_dc

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

Structured Systems Analysis - Decision Tables and Trees

Description

This reading material forms part of the "Decision Tables and Trees" topic in the Structured Systems Analysis module.

Subjects

ukoer | decision tables and trees reading material | decision trees | decision tables | decision trees and decision tables | systems analysis | structured systems analysis | system analysis | structured system analysis | decision table | decision tree | decision table and tree | decision trees reading material | decision tables reading material | systems analysis reading material | structured systems analysis reading material | system analysis reading material | structured system analysis reading material | decision table reading material | decision tree reading material | decision table and tree reading material | Computer science | I100

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Site sourced from

http://dspace.jorum.ac.uk/oai/request?verb=ListRecords&metadataPrefix=oai_dc

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

1.223J Transportation Policy, Strategy, and Management (MIT) 1.223J Transportation Policy, Strategy, and Management (MIT)

Description

This class surveys the current concepts, theories, and issues in strategic management of transportation organizations. It provides transportation logistics and engineering systems students with an overview of the operating context, leadership challenges, strategies, and management tools that are used in today's public and private transportation organizations. The following concepts, tools, and issues are presented in both public and private sector cases: alternative models of decision-making, strategic planning (e.g., use of SWOT analysis and scenario development), stakeholder valuation and analysis, government-based regulation and cooperation within the transportation enterprise, disaster communications, systems safety, change management, and the impact of globalization. This class surveys the current concepts, theories, and issues in strategic management of transportation organizations. It provides transportation logistics and engineering systems students with an overview of the operating context, leadership challenges, strategies, and management tools that are used in today's public and private transportation organizations. The following concepts, tools, and issues are presented in both public and private sector cases: alternative models of decision-making, strategic planning (e.g., use of SWOT analysis and scenario development), stakeholder valuation and analysis, government-based regulation and cooperation within the transportation enterprise, disaster communications, systems safety, change management, and the impact of globalization.

Subjects

public transportation systems; pollution; infrastructure; government regulation; public policy; strategic planning management; labor relations; maintenance planning; administration; financing; marketing policy; fare policy; management information; decision support systems; transit industry; service provision; private sector; alternative models of decision-making; strategic planning; stakeholder valuation and analysis; government-based regulation and cooperation; transportation enterprise; disaster communications; systems safety; change management; and the impact of globalization; | public transportation systems; pollution; infrastructure; government regulation; public policy; strategic planning management; labor relations; maintenance planning; administration; financing; marketing policy; fare policy; management information; decision support systems; transit industry; service provision; private sector; alternative models of decision-making; strategic planning; stakeholder valuation and analysis; government-based regulation and cooperation; transportation enterprise; disaster communications; systems safety; change management; and the impact of globalization; | public transportation systems | public transportation systems | pollution | pollution | infrastructure | infrastructure | government regulation | government regulation | public policy | public policy | strategic planning management | strategic planning management | labor relations | labor relations | maintenance planning | maintenance planning | administration | administration | financing | financing | marketing policy | marketing policy | fare policy | fare policy | management information | management information | decision support systems | decision support systems | transit industry | transit industry | service provision | service provision | private sector | private sector | alternative models of decision-making | alternative models of decision-making | strategic planning | strategic planning | stakeholder valuation and analysis | stakeholder valuation and analysis | government-based regulation and cooperation | government-based regulation and cooperation | transportation enterprise | transportation enterprise | disaster communications | disaster communications | systems safety | systems safety | change management | change management | and the impact of globalization | and the impact of globalization | the impact of globalization | the impact of globalization | 1.223 | 1.223 | ESD.203 | ESD.203

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

2.994 MADM with Applications in Material Selection and Optimal Design (MIT) 2.994 MADM with Applications in Material Selection and Optimal Design (MIT)

Description

This course begins with a comparative review of conventional and advanced multiple attribute decision making (MADM) models in engineering practice. Next, a new application of particular MADM models in reliable material selection of sensitive structural components as well as a multi-criteria Taguchi optimization method is discussed. Other specific topics include dealing with uncertainties in material properties, incommensurability in decision-makers opinions for the same design, objective ways of weighting performance indices, rank stability analysis, compensations and non-compensations. This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month. This course begins with a comparative review of conventional and advanced multiple attribute decision making (MADM) models in engineering practice. Next, a new application of particular MADM models in reliable material selection of sensitive structural components as well as a multi-criteria Taguchi optimization method is discussed. Other specific topics include dealing with uncertainties in material properties, incommensurability in decision-makers opinions for the same design, objective ways of weighting performance indices, rank stability analysis, compensations and non-compensations. This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

Subjects

materials selection | materials selection | tradeoff | tradeoff | optimization | optimization | Taguchi | Taguchi | multiple attribute | multiple attribute | decision making | decision making | multiple attribute decision making | multiple attribute decision making | performance index | performance index | rank stability analysis | rank stability analysis | decision matrix | decision matrix | multi-criteria decision making | multi-criteria decision making | multiobjective optimization | multiobjective optimization | Pareto | Pareto | TOPSIS | TOPSIS | ELECTRE | ELECTRE

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

1.223J Transportation Policy, Strategy, and Management (MIT) 1.223J Transportation Policy, Strategy, and Management (MIT)

Description

This class surveys the current concepts, theories, and issues in strategic management of transportation organizations. It provides transportation logistics and engineering systems students with an overview of the operating context, leadership challenges, strategies, and management tools that are used in today's public and private transportation organizations. The following concepts, tools, and issues are presented in both public and private sector cases: alternative models of decision-making, strategic planning (e.g., use of SWOT analysis and scenario development), stakeholder valuation and analysis, government-based regulation and cooperation within the transportation enterprise, disaster communications, systems safety, change management, and the impact of globalization. This class surveys the current concepts, theories, and issues in strategic management of transportation organizations. It provides transportation logistics and engineering systems students with an overview of the operating context, leadership challenges, strategies, and management tools that are used in today's public and private transportation organizations. The following concepts, tools, and issues are presented in both public and private sector cases: alternative models of decision-making, strategic planning (e.g., use of SWOT analysis and scenario development), stakeholder valuation and analysis, government-based regulation and cooperation within the transportation enterprise, disaster communications, systems safety, change management, and the impact of globalization.

Subjects

public transportation systems; pollution; infrastructure; government regulation; public policy; strategic planning management; labor relations; maintenance planning; administration; financing; marketing policy; fare policy; management information; decision support systems; transit industry; service provision; private sector; alternative models of decision-making; strategic planning; stakeholder valuation and analysis; government-based regulation and cooperation; transportation enterprise; disaster communications; systems safety; change management; and the impact of globalization; | public transportation systems; pollution; infrastructure; government regulation; public policy; strategic planning management; labor relations; maintenance planning; administration; financing; marketing policy; fare policy; management information; decision support systems; transit industry; service provision; private sector; alternative models of decision-making; strategic planning; stakeholder valuation and analysis; government-based regulation and cooperation; transportation enterprise; disaster communications; systems safety; change management; and the impact of globalization; | public transportation systems | public transportation systems | pollution | pollution | infrastructure | infrastructure | government regulation | government regulation | public policy | public policy | strategic planning management | strategic planning management | labor relations | labor relations | maintenance planning | maintenance planning | administration | administration | financing | financing | marketing policy | marketing policy | fare policy | fare policy | management information | management information | decision support systems | decision support systems | transit industry | transit industry | service provision | service provision | private sector | private sector | alternative models of decision-making | alternative models of decision-making | strategic planning | strategic planning | stakeholder valuation and analysis | stakeholder valuation and analysis | government-based regulation and cooperation | government-based regulation and cooperation | transportation enterprise | transportation enterprise | disaster communications | disaster communications | systems safety | systems safety | change management | change management | and the impact of globalization | and the impact of globalization | the impact of globalization | the impact of globalization | 1.223 | 1.223 | ESD.203 | ESD.203

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-alltraditionalchinesecourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | decision | decision-making | reasoning | optimization | autonomous | autonomous systems | decision support | algorithms | artificial intelligence | a.i. | operations | operations research | logic | deduction | heuristic search | constraint-based search | model-based reasoning | planning | execution | uncertainty | machine learning | linear programming | dynamic programming | integer programming | network optimization | decision analysis | decision theoretic planning | Markov decision process | scheme | propositional logic | constraints | Markov processes | computational performance | satisfaction | learning algorithms | system state | state | search treees | plan spaces | model theory | decision trees | function approximators | optimization algorithms | limitations | tradeoffs | search and reasoning | game tree search | local stochastic search | stochastic | genetic algorithms | constraint satisfaction | propositional inference | rule-based systems | rule-based | model-based diagnosis | neural nets | reinforcement learning | web-based | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

Site sourced from

https://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | decision | decision-making | reasoning | optimization | autonomous | autonomous systems | decision support | algorithms | artificial intelligence | a.i. | operations | operations research | logic | deduction | heuristic search | constraint-based search | model-based reasoning | planning | execution | uncertainty | machine learning | linear programming | dynamic programming | integer programming | network optimization | decision analysis | decision theoretic planning | Markov decision process | scheme | propositional logic | constraints | Markov processes | computational performance | satisfaction | learning algorithms | system state | state | search treees | plan spaces | model theory | decision trees | function approximators | optimization algorithms | limitations | tradeoffs | search and reasoning | game tree search | local stochastic search | stochastic | genetic algorithms | constraint satisfaction | propositional inference | rule-based systems | rule-based | model-based diagnosis | neural nets | reinforcement learning | web-based | search trees

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

Site sourced from

https://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

16.422 Human Supervisory Control of Automated Systems (MIT) 16.422 Human Supervisory Control of Automated Systems (MIT)

Description

Human Supervisory Control of Automated Systems discusses elements of the interactions between humans and machines.  These elements include: assignment of roles and authority; tradeoffs between human control and human monitoring; and human intervention in automatic processes.  Further topics comprise: performance, optimization and social implications of the system; enhanced human interfaces; decision aiding; and automated alterting systems.  Topics refer to applications in aerospace, industrial and transportation systems. Human Supervisory Control of Automated Systems discusses elements of the interactions between humans and machines.  These elements include: assignment of roles and authority; tradeoffs between human control and human monitoring; and human intervention in automatic processes.  Further topics comprise: performance, optimization and social implications of the system; enhanced human interfaces; decision aiding; and automated alterting systems.  Topics refer to applications in aerospace, industrial and transportation systems.

Subjects

Human supervisory control | Human supervisory control | Dynamic systems | Dynamic systems | Complex dynamic systems | Complex dynamic systems | Automation | Automation | Automated systems | Automated systems | Decision processes | Decision processes | Man-machine | Man-machine | Supervisory functions | Supervisory functions | Human-centered | Human-centered | Systems engineering design | Systems engineering design | Semi-structured models | Semi-structured models | Tast analysis | Tast analysis | Function allocation | Function allocation | Memory | Memory | Attention | Attention | Classical decision theory | Classical decision theory | Signal detection | Signal detection | Uncertainty | Uncertainty | Naturalistic decision making | Naturalistic decision making | Workload | Workload | Situation awareness | Situation awareness | Aircraft displays | Aircraft displays | Flight management systems | Flight management systems | Human error | Human error | Reliability | Reliability | Cooperative decision support | Cooperative decision support | Adaptive automation | Adaptive automation | Alerting systems | Alerting systems | Command and control | Command and control | Air traffic control | Air traffic control | Unmanned space vehicles | Unmanned space vehicles | Automobile systems | Automobile systems | Telemedicine | Telemedicine | Telerobotics | Telerobotics | Medical interface design | Medical interface design | Nuclear control plants | Nuclear control plants | Process control plants | Process control plants

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

ESD.72 Engineering Risk-Benefit Analysis (MIT) ESD.72 Engineering Risk-Benefit Analysis (MIT)

Description

ERBA (ESD.72) emphasizes three methodologies - reliability and probabilistic risk assessment (RPRA), decision analysis (DA), and cost-benefit analysis (CBA). In this class, the issues of interest are: the risks associated with large engineering projects such as nuclear power reactors, the International Space Station, and critical infrastructures; the development of new products; the design of processes and operations with environmental externalities; and infrastructure renewal projects. ERBA (ESD.72) emphasizes three methodologies - reliability and probabilistic risk assessment (RPRA), decision analysis (DA), and cost-benefit analysis (CBA). In this class, the issues of interest are: the risks associated with large engineering projects such as nuclear power reactors, the International Space Station, and critical infrastructures; the development of new products; the design of processes and operations with environmental externalities; and infrastructure renewal projects.

Subjects

risk analysis | risk analysis | decision analysis | decision analysis | uncertainty | uncertainty | cost-benefit analysis | cost-benefit analysis | remedial action alternative | remedial action alternative | probability | probability | utility functions | utility functions | environmental remediation | environmental remediation | risk aversion | risk aversion | multistage decision models | multistage decision models | axioms of rational behavior | axioms of rational behavior | design decisions | design decisions | fault-tolerant design | fault-tolerant design | risk management | risk management

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses-ESD.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

15.761 Operations Management (MIT) 15.761 Operations Management (MIT)

Description

This course will introduce concepts and techniques for design, planning and control of manufacturing and service operations. The course provides basic definitions of operations management terms, tools and techniques for analyzing operations, and strategic context for making operational decisions. We present the material in five modules: Operations Analysis Coordination and Planning Quality Management Project Management Logistics and Supply Chain Management This course will introduce concepts and techniques for design, planning and control of manufacturing and service operations. The course provides basic definitions of operations management terms, tools and techniques for analyzing operations, and strategic context for making operational decisions. We present the material in five modules: Operations Analysis Coordination and Planning Quality Management Project Management Logistics and Supply Chain Management

Subjects

manufacturing | manufacturing | service | service | analyzing operations | analyzing operations | operational decisions | operational decisions | operations analysis | operations analysis | quality management | quality management | project management | project management | logistics | logistics | supply chain management | supply chain management | job shop operations | job shop operations | process matching | process matching | queuing | queuing | forecasting | forecasting | queueing | queueing | analysis | analysis | analyzing | analyzing | operations | operations | coordination | coordination | planning | planning | quality | quality | project | project | management | management | supply chain | supply chain | job shop | job shop | decisions | decisions | decision making | decision making | operational | operational | design | design | control | control | materials | materials | production | production | scheduling | scheduling | reengineering | reengineering | capacity | capacity | facilities | facilities | strategy | strategy | process | process | processes | processes | matching | matching | inventory | inventory | vendor | vendor | customer | customer

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

6.231 Dynamic Programming and Stochastic Control (MIT) 6.231 Dynamic Programming and Stochastic Control (MIT)

Description

This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages (finite and infinite horizon). We will also discuss some approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations. This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages (finite and infinite horizon). We will also discuss some approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations.

Subjects

dynamic programming | dynamic programming | stochastic control | stochastic control | decision making | decision making | uncertainty | uncertainty | sequential decision making | sequential decision making | finite horizon | finite horizon | infinite horizon | infinite horizon | approximation methods | approximation methods | state space | state space | large state space | large state space | optimal control | optimal control | dynamical system | dynamical system | dynamic programming and optimal control | dynamic programming and optimal control | deterministic systems | deterministic systems | shortest path | shortest path | state information | state information | rollout | rollout | stochastic shortest path | stochastic shortest path | approximate dynamic programming | approximate dynamic programming

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

1.010 Uncertainty in Engineering (MIT) 1.010 Uncertainty in Engineering (MIT)

Description

This undergraduate class serves as an introduction to probability and statistics, with emphasis on engineering applications. The first segment discusses events and their probability, Bayes' Theorem, discrete and continuous random variables and vectors, univariate and multivariate distributions, Bernoulli trials and Poisson point processes, and full-distribution uncertainty propagation and conditional analysis. The second segment deals with second-moment representation of uncertainty and second-moment uncertainty propagation and conditional analysis. The final segment covers random sampling, point and interval estimation, hypothesis testing, and linear regression. Many of the concepts covered in class are illustrated with real-world examples from various areas of engineering. This undergraduate class serves as an introduction to probability and statistics, with emphasis on engineering applications. The first segment discusses events and their probability, Bayes' Theorem, discrete and continuous random variables and vectors, univariate and multivariate distributions, Bernoulli trials and Poisson point processes, and full-distribution uncertainty propagation and conditional analysis. The second segment deals with second-moment representation of uncertainty and second-moment uncertainty propagation and conditional analysis. The final segment covers random sampling, point and interval estimation, hypothesis testing, and linear regression. Many of the concepts covered in class are illustrated with real-world examples from various areas of engineering.

Subjects

statistics | statistics | decision analysis | decision analysis | random variables and vectors | random variables and vectors | uncertainty propagation | uncertainty propagation | conditional distributions | conditional distributions | second-moment analysis | second-moment analysis | system reliability | system reliability | Bayesian analysis and risk-based decision | Bayesian analysis and risk-based decision | estimation of distribution parameters | estimation of distribution parameters | hypothesis testing | hypothesis testing | simple and multiple linear regressions | simple and multiple linear regressions | Poisson and Markov processes | Poisson and Markov processes

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

14.452 Macroeconomic Theory II (MIT) 14.452 Macroeconomic Theory II (MIT)

Description

This is the second course in the four-quarter graduate sequence in macroeconomics. Its purpose is to introduce the basic models macroeconomists use to study fluctuations. The course is organized around nine topics/sections: Fluctuations and Facts; The basic model: the consumption/saving choice; Allowing for a labor/leisure choice (the RBC model); Allowing for non trivial investment decisions; Allowing for two goods; Introducing money; Introducing price setting; Introducing staggering of price decisions; and Applications to fiscal and monetary policy. This is the second course in the four-quarter graduate sequence in macroeconomics. Its purpose is to introduce the basic models macroeconomists use to study fluctuations. The course is organized around nine topics/sections: Fluctuations and Facts; The basic model: the consumption/saving choice; Allowing for a labor/leisure choice (the RBC model); Allowing for non trivial investment decisions; Allowing for two goods; Introducing money; Introducing price setting; Introducing staggering of price decisions; and Applications to fiscal and monetary policy.

Subjects

Economics | Economics | Macroeconomics | Macroeconomics | macroeconomics | macroeconomics | fluctuations | fluctuations | consumption | consumption | saving | saving | choice | choice | labor | labor | leisure | leisure | RBC model | RBC model | non trivial investment decisions | non trivial investment decisions | money | money | price setting | price setting | staggering price decisions | staggering price decisions | fiscal policy | fiscal policy | monetary policy. | monetary policy. | monetary policy | monetary policy

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

6.451 Principles of Digital Communication II (MIT) 6.451 Principles of Digital Communication II (MIT)

Description

This course is the second of a two-term sequence with 6.450. The focus is on coding techniques for approaching the Shannon limit of additive white Gaussian noise (AWGN) channels, their performance analysis, and design principles. After a review of 6.450 and the Shannon limit for AWGN channels, the course begins by discussing small signal constellations, performance analysis and coding gain, and hard-decision and soft-decision decoding. It continues with binary linear block codes, Reed-Muller codes, finite fields, Reed-Solomon and BCH codes, binary linear convolutional codes, and the Viterbi algorithm.More advanced topics include trellis representations of binary linear block codes and trellis-based decoding; codes on graphs; the sum-product and min-sum algorithms This course is the second of a two-term sequence with 6.450. The focus is on coding techniques for approaching the Shannon limit of additive white Gaussian noise (AWGN) channels, their performance analysis, and design principles. After a review of 6.450 and the Shannon limit for AWGN channels, the course begins by discussing small signal constellations, performance analysis and coding gain, and hard-decision and soft-decision decoding. It continues with binary linear block codes, Reed-Muller codes, finite fields, Reed-Solomon and BCH codes, binary linear convolutional codes, and the Viterbi algorithm.More advanced topics include trellis representations of binary linear block codes and trellis-based decoding; codes on graphs; the sum-product and min-sum algorithms

Subjects

coding techniques | coding techniques | the Shannon limit of additive white Gaussian noise channels | the Shannon limit of additive white Gaussian noise channels | performance analysis | performance analysis | Small signal constellations | Small signal constellations | coding gain | coding gain | Hard-decision and soft-decision decoding | Hard-decision and soft-decision decoding | Introduction to binary linear block codes | Introduction to binary linear block codes | Reed-Muller codes | Reed-Muller codes | finite fields | finite fields | Reed-Solomon and BCH codes | Reed-Solomon and BCH codes | binary linear convolutional codes | binary linear convolutional codes | Viterbi and BCJR algorithms | Viterbi and BCJR algorithms | Trellis representations of binary linear block codes | Trellis representations of binary linear block codes | trellis-based ML decoding | trellis-based ML decoding | Codes on graphs | Codes on graphs | sum-product | sum-product | max-product | max-product | decoding algorithms | decoding algorithms | Turbo codes | Turbo codes | LDPC codes and RA codes | LDPC codes and RA codes | Coding for the bandwidth-limited regime | Coding for the bandwidth-limited regime | Lattice codes | Lattice codes | Trellis-coded modulation | Trellis-coded modulation | Multilevel coding | Multilevel coding | Shaping | Shaping

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

17.559 Comparative Security and Sustainability (MIT) 17.559 Comparative Security and Sustainability (MIT)

Description

This course focuses on the complexities associated with security and sustainability of states in international relations. Covering aspects of theory, methods and empirical analysis, the course is in three parts, and each consists of seminar sessions focusing on specific topics. This course focuses on the complexities associated with security and sustainability of states in international relations. Covering aspects of theory, methods and empirical analysis, the course is in three parts, and each consists of seminar sessions focusing on specific topics.

Subjects

security; sustainability; international relations; comparative approaches; constraints; options; strategies; policy choice; developing and industrial nations; decision; trade-offs; inter-temporal effects; technology; design systems; | security; sustainability; international relations; comparative approaches; constraints; options; strategies; policy choice; developing and industrial nations; decision; trade-offs; inter-temporal effects; technology; design systems; | security | security | sustainability | sustainability | international relations | international relations | comparative approaches | comparative approaches | constraints | constraints | options | options | strategies | strategies | policy choice | policy choice | developing and industrial nations | developing and industrial nations | decision | decision | trade-offs | trade-offs | inter-temporal effects | inter-temporal effects | technology | technology | design systems | design systems

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses-17.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

6.451 Principles of Digital Communication II (MIT) 6.451 Principles of Digital Communication II (MIT)

Description

Includes audio/video content: AV lectures. This course is the second of a two-term sequence with 6.450. The focus is on coding techniques for approaching the Shannon limit of additive white Gaussian noise (AWGN) channels, their performance analysis, and design principles. After a review of 6.450 and the Shannon limit for AWGN channels, the course begins by discussing small signal constellations, performance analysis and coding gain, and hard-decision and soft-decision decoding. It continues with binary linear block codes, Reed-Muller codes, finite fields, Reed-Solomon and BCH codes, binary linear convolutional codes, and the Viterbi algorithm. More advanced topics include trellis representations of binary linear block codes and trellis-based decoding; codes on graphs; the sum-product and Includes audio/video content: AV lectures. This course is the second of a two-term sequence with 6.450. The focus is on coding techniques for approaching the Shannon limit of additive white Gaussian noise (AWGN) channels, their performance analysis, and design principles. After a review of 6.450 and the Shannon limit for AWGN channels, the course begins by discussing small signal constellations, performance analysis and coding gain, and hard-decision and soft-decision decoding. It continues with binary linear block codes, Reed-Muller codes, finite fields, Reed-Solomon and BCH codes, binary linear convolutional codes, and the Viterbi algorithm. More advanced topics include trellis representations of binary linear block codes and trellis-based decoding; codes on graphs; the sum-product and

Subjects

coding techniques | coding techniques | the Shannon limit of additive white Gaussian noise channels | the Shannon limit of additive white Gaussian noise channels | performance analysis | performance analysis | Small signal constellations | Small signal constellations | coding gain | coding gain | Hard-decision and soft-decision decoding | Hard-decision and soft-decision decoding | Introduction to binary linear block codes | Introduction to binary linear block codes | Reed-Muller codes | Reed-Muller codes | finite fields | finite fields | Reed-Solomon and BCH codes | Reed-Solomon and BCH codes | binary linear convolutional codes | binary linear convolutional codes | Viterbi and BCJR algorithms | Viterbi and BCJR algorithms | Trellis representations of binary linear block codes | Trellis representations of binary linear block codes | trellis-based ML decoding | trellis-based ML decoding | Codes on graphs | Codes on graphs | sum-product | sum-product | max-product | max-product | decoding algorithms | decoding algorithms | Turbo codes | Turbo codes | LDPC codes and RA codes | LDPC codes and RA codes | Coding for the bandwidth-limited regime | Coding for the bandwidth-limited regime | Lattice codes. | Lattice codes. | Trellis-coded modulation | Trellis-coded modulation | Multilevel coding | Multilevel coding | Shaping | Shaping | Lattice codes | Lattice codes

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allavcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

1.010 Uncertainty in Engineering (MIT) 1.010 Uncertainty in Engineering (MIT)

Description

This course gives an introduction to probability and statistics, with emphasis on engineering applications. Course topics include events and their probability, the total probability and Bayes' theorems, discrete and continuous random variables and vectors, uncertainty propagation and conditional analysis. Second-moment representation of uncertainty, random sampling, estimation of distribution parameters (method of moments, maximum likelihood, Bayesian estimation), and simple and multiple linear regression. Concepts illustrated with examples from various areas of engineering and everyday life. This course gives an introduction to probability and statistics, with emphasis on engineering applications. Course topics include events and their probability, the total probability and Bayes' theorems, discrete and continuous random variables and vectors, uncertainty propagation and conditional analysis. Second-moment representation of uncertainty, random sampling, estimation of distribution parameters (method of moments, maximum likelihood, Bayesian estimation), and simple and multiple linear regression. Concepts illustrated with examples from various areas of engineering and everyday life.

Subjects

fundamentals of probability | fundamentals of probability | random processes | random processes | statistics | statistics | decision analysis | decision analysis | random variables and vectors | random variables and vectors | uncertainty propagation | uncertainty propagation | conditional distributions | conditional distributions | second-moment analysis | second-moment analysis | system reliability | system reliability | Bayes theorem | Bayes theorem | total probability theorem | total probability theorem | Bayesian analysis and risk-based decision | Bayesian analysis and risk-based decision | estimation of distribution parameters | estimation of distribution parameters | hypothesis testing | hypothesis testing | simple and multiple linear regressions | simple and multiple linear regressions | Poisson and Markov processes | Poisson and Markov processes

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata