 Description:
| Aims & Learning Objectives:
Aims:
To introduce students to the ideas and techniques that underpin the theory and practice of the Bayesian approach to statistics.
Objectives:
Students should be able to formulate the Bayesian treatment and analysis of many familiar statistical problems.
Content:
Bayesian methods provide an alternative approach to data analysis, which has the ability to incorporate prior knowledge about a parameter of interest into the statistical model. The prior knowledge takes the form of a prior (to sampling) distribution on the parameter space, which is updated to a posterior distribution via Bayes' Theorem, using the data. Summaries about the parameter are described using the posterior distribution. The Bayesian Paradigm; decision theory; utility theory; exchangeability; Representation Theorem; prior, posterior and predictive distributions; conjugate priors. Tools to undertake a Bayesian statistical analysis will also be introduced. Simulation based methods such as Markov Chain Monte Carlo and importance sampling for use when analytical methods fail.
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