The aim of this unit is 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. This unit covers: 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 non-informative 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.
8-12 hours total study time per week, distance learning
Assignments 60% (2x 30%) and submitted exercises (40%)
Gelman A, Carlin JB, Stern HS, Rubin DB, Dunson DB, Vehtari A. Bayesian Data Analysis, 3rd edition. Chapman and Hall, 2003. ISBN 978-1-58488-388-3; Notes provided.
This unit of study is only offered in even numbered years. It is available in 2018.
BSTA5008 and (PUBH5010 or BSTA5011 or CEPI5100)