Covers discrete-time Markov chain theory; inference for discrete-time Markov chains; Monte Carlo methods; missing data; hidden Markov models; and Gaussian Markov random fields. Prerequisite: either STAT 342 or STAT 396. Offered: A.
The purpose of this course is to introduce students to the art of stochastic modeling. The theoretical component of the course covers material standard for a first course in stochastic processes. However, emphasis on statistical inference and scientifically motivated examples give a unique flavor to the mathematics presented in the course. The first quarter of the Stochastic Modeling sequence will be devoted to discrete and continuous-time Markov chains on countable state spaces.
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