Bayesian Inference With Ecological Applications

About this book

Statistical theory is primarily a product of the twentieth century. The prevailing school of thought builds on the frequentist philosophy developed by R.A. Fisher, the eminent biological theorist and experimentalist. Fisher's philosophy has been so thoroughly embraced that it has been labeled the 'classical' approach, even though the alternative Bayesian philosophy antedates it by more than a century.

Frequentist thinking has prevailed over Bayesian primarily because of the practical difficulty of fitting all but the simplest Bayesian models. Wildlife statistics has been almost entirely conducted in the frequentist mode. However, wildlife data are most naturally described in terms of hierarchical models, and these models are best analyzed using Bayesian tools. The advent of fast personal computers and easily available software has nearly removed the difficulties in fitting Bayesian models, and hierarchical models in particular. Hierarchical models describe stochastic population processes governing data and these processes are the real focus of scientific inquiry.

This book takes the reader into the domain of Bayesian inference where complex hierarchical modelling is made possible. It features engagingly written text specifically designed to demystifying a complex subject. It includes examples drawn from ecology and wildlife research. It offers an essential grounding for graduate and research ecologists in this rapidly adopted statistical technique. It also features a companion website with analytical software and examples. It is written by leading authors with world class reputations in Ecology and Biostatistics.

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