The Bayesian approach to statistics and decision analysis has now established itself as a powerful and very practical tool to support the non-statistical, but otherwise technical modeller. Recent advances in computational techniques have released its potential to an extent where even very complicated scenarios are amenable to Bayesian analysis whether conducted by the statistical community or others. The purpose of this book is twofold. Firstly it has been written to advertise the advantages a Bayesian analysis can bring. New statistical and decision models can be tailored to the unique beliefs, values and needs of the user and the implications of the data she collects can be analyzed with reference to this underlying structure. The second purpose is to provide an analyst, wanting to apply the Bayesian methodology, with a resource of examples and techniques so she may be better informed about the scope of opportunities and the pitfalls inherent in such an analysis.
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