6.041 Probabilistic Systems Analysis


Class Info

An introduction to probability theory, and the modeling and analysis of probabilistic systems. Probabilistic models, conditional probability. Discrete and continuous random variables. Expectation and conditional expectation. Limit Theorems. Bernoulli and Poisson processes. Markov chains. Bayesian estimation and hypothesis testing. Elements of statistical inference. Meets with graduate subject 6.431, but assignments differ.

This class has 18.02 as a prerequisite.

6.041 will be offered this semester (Fall 2017). It is instructed by D. P. Bertsekas and J. N. Tsitsiklis.

Lecture occurs 12:00 PM to 13:00 PM on Mondays and Wednesdays in 54-100.

This class counts for a total of 12 credits.

You can find more information on MIT OpenCourseWare at the Probabilistic Systems Analysis and Applied Probability site or on the 6.041 Stellar site.

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MIT 6.041 Probabilistic Systems Analysis Related Textbooks
MIT 6.041 Probabilistic Systems Analysis On The Web
Probabilistic Systems Analysis and Applied Probability
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