Courses / Module

BAYESIAN DATA ANALYSIS (R)

Module code: ST845
Credits: 5
Semester: 1
Department: MATHEMATICS AND STATISTICS
International:

	Overview
	Overview -------- Bayesian Analysis for simple models: Bayes' rule, inferring a binomial probability, prior distributions, Markov Chain Monte Carlo, Gibbs sampler, assessing model convergence, Bayesian analysis with JAGS, simple regression models, hierarchical/multilevel models, generalized linear models, model checking. Lectures and tutorials as for ST405. For Registered Research students. Permission is required from the Head of the Mathematics & Statistics Department.

Overview

Overview
--------
Bayesian Analysis for simple models: Bayes' rule, inferring a binomial probability, prior distributions, Markov Chain Monte Carlo, Gibbs sampler, assessing model convergence, Bayesian analysis with JAGS, simple regression models, hierarchical/multilevel models, generalized linear models, model checking.

Lectures and tutorials as for ST405.

For Registered Research students. Permission is required from the Head of the Mathematics & Statistics Department.

	Learning Outcomes
	On successful completion of the module, students should be able to: Understand the concepts of prior and posterior distributions. Compare Bayesian and classical models. Implement Bayesian models and apply them to problems in data science.

Teaching & Learning methods

Assessment

	Autumn Supplementals/Resits
	Are supplemental/resit registrations permitted? No

	Pre-Requisites
	This module is for Registered Research students. Permission is required from the Head of the Mathematics and Statistics Department to take this module.

	Timetable
	This timetable is not currently available, please contact the Mathematics And Statistics Department.

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Courses / Module

BAYESIAN DATA ANALYSIS (R)