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BAYESIAN DATA ANALYSIS (R)

Module code: ST845
Credits: 5
Semester: 1
Quota: Restricted entry, contact department for details before registering
Department: MATHEMATICS AND STATISTICS
International:

	Overview
	Overview -------- Bayesian analysis for simple models: conjugate prior distributions, inference for parameters of normal, binomial, Poisson data. Linear regression model. Generalized linear models, hierarchical models. Simulation based Bayesian analysis: using Bugs/STAN from R. Predictive distribution and 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: conjugate prior distributions, inference for parameters of normal, binomial, Poisson data. Linear regression model. Generalized linear models, hierarchical models. Simulation based Bayesian analysis: using Bugs/STAN from R. Predictive distribution and 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.