Bayesian Methods for Ecology

Features

• Cover Type: Paperback with 310 pages
• Published by: Cambridge University Press
• Edition: 1st Edition May 14, 2007
• Written in: English
• ISBN 10 Number: 0521615593
• ISBN 13 Number: 978-0521615594
• Book Dimensions: 8.8 x 6 x 0.7 inches
• Weighs: 1.1 pounds

Product Description
The interest in using Bayesian methods in ecology is increasing, however many ecologists have difficulty with conducting the required analyses. McCarthy bridges that gap, using a clear and accessible style. The text also incorporates case studies to demonstrate mark-recapture analysis, development of population models and the use of subjective judgement. The advantages of Bayesian methods, are also described here, for example, the incorporation of any relevant prior information and the ability to assess the evidence in favour of competing hypotheses. Free software is available as well as an accompanying web-site containing the data files and WinBUGS codes. Bayesian Methods for Ecology will appeal to academic researchers, upper undergraduate and graduate students of Ecology.

Book Description
Statistical analysis of data is fundamentally important in ecology but most ecologists have difficulty with conducting analyses. McCarthy describes how to use Bayesian methods to analyse a wide range of data and models in ecology. This accessible text will appeal to academic researchers, upper undergraduate and graduate students of Ecology.

Reader Reviews
I recently used this book for a class, and found it a good introduction for those already well familiar with classical frequentist techniques in ecology. It provides good cookbook recipes for doing an ANOVA, regression, etc with BUGS. The first chapter also cites some good reasons as to WHY one should use Bayes when they have prior information over other techniques. This was NOT however, a book that was particularly useful for those who want a thorough introduction to Bayes, warts and all, as well as giving one a real thorough understanding of Hierarchical modeling, which is arguably where Bayes can see it's best applications, particularly in ecology. The class as a whole felt like the book needs more thorough examples, more explanations of the WHYs, as well as the potential pitfalls, how to find problems with your models, as well as some applied problem sets. We also used Gelman and Hill's book, which has been excellent as a more thorough introduction, even if it is in the social sciences, and I have high hopes for James Clark's new book, although I have not seen a copy yet.