STSCI 4780

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  • Schedule of Classes - February 7, 2022 11:35AM EST
  • Course Catalog - January 18, 2022 1:31PM EST

STSCI 4780
Bayesian Data Analysis: Principles and Practice

Course information provided by the Courses of Study 2021-2022.

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.

When Offered Spring.

Prerequisites/Corequisites Prerequisite: MATH 2130, MATH 2210, CS 1110 or equivalents. 

Distribution Category (MQR-AS, SDS-AS)

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.

View Enrollment Information

Syllabi: none

  •   Regular Academic Session.  Choose one lecture and one laboratory. Combined with: STSCI 5780

  • 4 Credits Stdnt Opt

  • 17359 STSCI 4780   LEC 001

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

  • 17360 STSCI 4780   LAB 401

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    • F Warren Hall B75
    • Jan 24 - May 10, 2022

    • Loredo, T

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