Bayesian Statistical Methods (BAY)

To achieve an understanding of the logic of Bayesian statistical inference, i.e. the use of probability models to quantify uncertainty in statistical conclusions, and acquire skills to perform practical Bayesian analysis relating to health research problems.


Annual Availability

BAY is delivered in alternate (even) years.

Coordinator

Prof Lyle Gurrin, Melbourne School of Population & Global Health, University of Melbourne


COORDINATORS:
Prof Lyle Gurrin University of Melbourne, School of Population and Global Health Semester 2
General outline

Prerequisites

Epidemiology, Mathematical Background for Biostatistics, Probability and Distribution Theory, Principles of Statistical Inference, Linear Models, Categorical Data and Generalised Linear Models

Time commitment

8-12 hours total study time per week

Semester availability

Semester 2 in year of delivery (offered in even years)

Assessment

Assignments 60% (two major assignments worth 30% each) and submission of selected practical exercises 40%

Prescribed Texts

Gelman A, Carlin JB, Stern HS, Rubin DB, Dunson DB, Vehtari A Bayesian Data Analysis, 3rd edition. Chapman and Hall 2013 For details, including ISBN, see the BCA Textbook and Software Guide

Special Computer Requirements

Microsoft Excel, Stata or R for simple calculations. R for simulations and model fitting using MCMC routines

Content

Topics include simple one-parameter models with conjugate prior distributions; standard models containing two or more parameters, including specifics for the normal location-scale model; the role of noninformative prior distributions; the relationship between Bayesian methods and standard “classical” approaches to statistics, especially those based on likelihood methods; computational techniques for use in Bayesian analysis, especially the use of simulation from posterior distributions, with emphasis on the WinBUGS package as a practical tool; application of Bayesian methods for fitting hierarchical models to complex data structures.

Special Computer Requirements

Course notes, assignment material and interaction facilities available online