STSCI 4780

STSCI 4780

Course information provided by the Courses of Study 2019-2020.

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)

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Enrollment Information
Syllabi: none
  •   Regular Academic Session.  Choose one lecture and one laboratory.

  • 4 Credits Stdnt Opt

  • 17918STSCI 4780  LEC 001

    • TRUpson Hall 216
    • Jan 21 - May 5, 2020
    • Loredo, T

  • Instruction Mode: Hybrid - Online & 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).

  • 17921STSCI 4780  LAB 401

    • FUpson Hall 216
    • Jan 21 - May 5, 2020
    • Loredo, T

  • Instruction Mode: Hybrid - Online & 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).