Courses of Study 2018-2019 
    Aug 10, 2022  
Courses of Study 2018-2019 [ARCHIVED CATALOG]

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STSCI 4780 - Bayesian Data Analysis: Principles and Practice

Spring. 4 credits. Student option grading.

Prerequisite: 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.

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