Probability theory: special univariate distributions; multivariate, marginal and conditional distributions. Introduction to stochastic processes, Markov chain, expectation and generating functions, functions of random variables and derived distributions,random sums, convergence of random sequence, sampling distributions, basic methods of estimation; maximum liklelihood estimation (MLE) and multiple model estimation (MME), unbiased, and consistency. Introduction to decision theory, Minimax and Bayes approach, structure of Bayes and Minimax rules, complete class of rules, point and interval estimations as a decision problem, and hypothesis testing as a decision problem. -- Course Website
Prerequisites: 310534 (v.1)<br/> Statistical Data Analysis 502<br/> <br/> or any previous version
