6.041 Probabilistic Systems Analysis
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.
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.
© Copyright 2015 Yasyf Mohamedali