STATS 451 - Bayesian Data Analysis
Fall 2019, Section 001
Instruction Mode: Section 001 is  In Person (see other Sections below)
Subject: Statistics (STATS)
Department: LSA Statistics

Details

Credits:
3
Requirements & Distribution:
BS
Waitlist Capacity:
99
Enforced Prerequisites:
(STATS 412 or STATS 425) and (STATS 306 or EECS 280).
BS:
This course counts toward the 60 credits of math/science required for a Bachelor of Science degree.
Repeatability:
May not be repeated for credit.
Primary Instructor:

Description

The course is an introduction to both the principles and practice of Bayesian inference for data analysis. At the end of this course students will be familiar with the Bayesian paradigm, and will be able to analyze different classes of statistical models. The course gives an introduction to the computational tools needed for Bayesian data analysis and develops statistical modeling skills through a hands-on data analysis approach. Topics include: prior/posterior distributions, Bayes rule, Markov Chain Monte Carlo computations, linear and generalized linear models, mixed effect models, hierarchical models, analysis of spatial data, model selection and comparison, model checking.

Schedule

STATS 451 - Bayesian Data Analysis
Schedule Listing
001 (LEC)
 In Person
32603
Closed
0
 
-
MW 8:30AM - 10:00AM

Textbooks/Other Materials

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Syllabi

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