On completion students should:
1. have a thorough understanding of Bayesian statistical inference;
2. be able to compare Bayesian methods to standard statistical methods;
3. be able to apply appropriate Bayesian statistical methods to the analysis of health/medical related data.

The first component of the course is an introduction to simple one-parameter models with conjugate prior distributions, which are fundamental to Bayesian statistics. This knowledge is built upon by introducing students to standard models containing two or more parameters, including specifics for the normal location-scale model. Once students have this knowledge, they will then be shown the relationship between Bayesian methods and the standard approaches to statistics. The next component of the course will provide students with practical experience using computational techniques for Bayesian analyses via common statistical software. Finally, students will be exposed to the application of Bayesian methods for fitting hierarchical models to complex data structures.

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Epidemiology (EPID6420);
Mathematical Background for Biostatistics (BIOS6040);
Principles of Statistical Inference (BIOS6050);
Linear Models (BIOS6070);
Categorical Data & GLMs (BIOS6020);
Probability and Distribution Theory (BIOS6170);

Distance Learning : Paper Based

Self Directed Learning

Self Directed Learning: for 6 hour(s) per Week for Full Term