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Instructor Class Description

Time Schedule:

Volodymyr Minin
STAT 516
Seattle Campus

Stochastic Modeling of Scientific Data

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.

Class description

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.

Student learning goals

General method of instruction

Recommended preparation

Class assignments and grading


The information above is intended to be helpful in choosing courses. Because the instructor may further develop his/her plans for this course, its characteristics are subject to change without notice. In most cases, the official course syllabus will be distributed on the first day of class.
Last Update by Volodymyr Minin
Date: 08/18/2008