Courses :: Module, BAYESIAN DATA ANALYSIS

	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. ST645 is equivalent to ST405 (i.e. lectures and tutorials as for ST405).

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.

ST645 is equivalent to ST405 (i.e. lectures and tutorials as for ST405).

	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.

Learning Outcomes

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

Autumn Supplementals/Resits

	Co-Requisites
	St633 or equivalent

Co-Requisites

	Timetable
	Click on the link to open the Timetable for Module [ST645]. Venue locations can be found here.

Timetable

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BAYESIAN DATA ANALYSIS