STSCI 4780 - Bayesian Data Analysis: Principles and Practice

(MQR-AS, SDS-AS)

Spring. 4 credits. Student option grading.

Prerequisite: MATH 2130, MATH 2210, CS 1110 or equivalents. Co-meets with STSCI 5780.

T. Loredo.

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.

Outcome 1: A basic understanding of the principles and foundations underlying the Bayesian approach.

Outcome 2: Practical experience using basic/intermediate Bayesian methods.

Outcome 3: Experience with widely-used tools and software development practices for producing and sharing collaborative, reproducible statistical research.