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Spring 2018
Mar 29, 2024
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Information Select the Course Number to get further detail on the course. Select the desired Schedule Type to find available classes for the course.

CSI 674 - Bayesian Inference/Dec Theory
Introduces decision theory and relationship to Bayesian statistical inference. Teaches commonalities, differences between Bayesian and frequentist approaches to statistical inference, how to approach statistics problem, and how to combine data with informed expert judgment to derive useful and policy relevant conclusions. Teaches theory to develop understanding of when and how to apply Bayesian and frequentist methods; and practical procedures for inference, hypothesis testing, and developing statistical models for phenomena. Teaches fundamentals of Bayesian theory of inference, including probability as a representation for degrees of belief, likelihood principle, use of Bayes Rule to revise beliefs based on evidence, conjugate prior distributions for common statistical models, and methods for approximating the posterior distribution. Introduces graphical models for constructing complex probability and decision models from modular components.
3.000 Credit hours
3.000 Lecture hours

Levels: Graduate, Non-Degree, Undergraduate, Washington Consortium
Schedule Types: Lecture

Computational & Data Sciences Department

Course Attributes:
Graduate - First


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