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.
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"[This book] will advance any ecologists' understanding of Bayesian statistics. ... the many diverse examples, which are the book's greatest strength, make the topic very approachable, even for people with moderate understanding of statistical theory. ... I therefore would highly recommend it to any ecologist interested in learning more about Bayesian statistics, and especially to those who want to learn to run Bayesian analyses in Win BUGS." - Tabitha Graves, Ecology
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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.
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16 of 16 people found the following review helpful:
3.0 out of 5 stars
Good BUGS cookbook,
This review is from: Bayesian Methods for Ecology (Paperback)
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.
1 of 1 people found the following review helpful:
5.0 out of 5 stars
Great for learning WinBugs,
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This review is from: Bayesian Methods for Ecology (Paperback)
This book is a great little book for learning some code for programming in WinBUGS. I used it to supplement a course in Bayesian Data Analysis.
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refuge density, silly nulls, spinifex cover, posterior deviance, using uninformative priors, starling data, minimum deviance, brown tree frog, more extreme data, credible interval, using subjective judgement, null hypothesis significance testing, median dispersal distance, powerful owls, information theoretic methods, categorical distribution, null hypothesis testing, relevant prior information, frequentist methods, frequentist analysis, frequentist confidence intervals, mountain pygmy possums, nocturnal searches, surveying problem, posterior distribution Key Phrases - Capitalized Phrases (CAPs): (learn more)
Node Mean, Sally Clark, Monte Carlo, Mullan Crain, Specification Tool, Parameters Mean, Sample Monitor Tool, New York, Snake Island New!
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refuge density, silly nulls, spinifex cover, posterior deviance, using uninformative priors, starling data, minimum deviance, brown tree frog, more extreme data, credible interval, using subjective judgement, null hypothesis significance testing, median dispersal distance, powerful owls, information theoretic methods, categorical distribution, null hypothesis testing, relevant prior information, frequentist methods, frequentist analysis, frequentist confidence intervals, mountain pygmy possums, nocturnal searches, surveying problem, posterior distribution Key Phrases - Capitalized Phrases (CAPs): (learn more)
Node Mean, Sally Clark, Monte Carlo, Mullan Crain, Specification Tool, Parameters Mean, Sample Monitor Tool, New York, Snake Island New!
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Front Cover | Table of Contents | First Pages | Index | Surprise Me!
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