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| CSI 674 - Bayesian Inference/Dec Theory |
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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 Restrictions: Must be enrolled in one of the following Levels: Non-Degree Undergraduate Graduate May not be enrolled in one of the following Degrees: Non-Degree Undergraduate Must be enrolled in one of the following Classifications: Senior Plus Non-Degree Advanced to Candidacy Graduate |
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