STSCI 4780

Bayesian Data Analysis: Principles and Practice

Course Description

Course information provided by the 2025-2026 Catalog.

Bayesian data analysis uses probability theory as a kind of calculus of inference, specifying how to quantify and propagate uncertainty in data-based chains of reasoning. Students will learn the fundamental principles of Bayesian data analysis, and how to apply them to varied data analysis problems across science and engineering. Topics include: basic probability theory, Bayes's theorem, linear and nonlinear models, hierarchical and graphical models, basic decision theory, and experimental design. There will be a strong computational component, using a high-level language such as R or Python, and a probabilistic language such as BUGS or Stan.

Prerequisites BTRY 3080 and BTRY 3020/STSCI 3200, or equivalent.

Distribution Requirements (DLG-AG, OPHLS-AG), (SDS-AS)

Last 4 Terms Offered 2022SP, 2020SP, 2018SP, 2015SP

Learning Outcomes

• A basic understanding of the principles and foundations underlying the Bayesian approach.
• Practical experience using basic/intermediate Bayesian methods.
• Experience with widely-used tools and software development practices for producing and sharing collaborative, reproducible statistical research.

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