STSCI 4780 - Bayesian Data Analysis: Principles and Practice

(MQR)

Spring. 4 credits.

Prerequisites: Basic multivariate differential and integral calculus (e.g., MATH 1120 or MATH 2220), basic linear algebra (e.g., MATH 2210, MATH 2310 or MATH 2940), familiarity with some programming language or numerical computing environment (like R, Python, MATLAB, Octave, IDL).

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.