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Title : 6.041 Probabilistic Systems Analysis and Applied Probability (MIT)

Description : This course is offered both to undergraduates (6.041) and graduates (6.431), but the assignments differ. 6.041/6.431 introduces students to the modeling, quantification, and analysis of uncertainty. Topics covered include: formulation and solution in sample space, random variables, transform techniques, simple random processes and their probability distributions, Markov processes, limit theorems, and elements of statistical inference.

Fromsemester : Spring

Fromyear : 2005

Creator :

Creator :

Creator :

Date : 2012-05-17T03:34:33+05:00

Relation : 6.041

Relation : 6.431

Language : en-US

Subject : probabilistic systems

Subject : probabilistic systems analysis

Subject : applied probability

Subject : uncertainty

Subject : uncertainty modeling

Subject : uncertainty quantification

Subject : analysis of uncertainty

Subject : uncertainty analysis

Subject : sample space

Subject : random variables

Subject : transform techniques

Subject : simple random processes

Subject : probability distribution

Subject : Markov process

Subject : limit theorem

Subject : statistical inference

Publisher : MIT OpenCourseWare

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