Articoli correlati a Bayesian Methods for Ecology

Bayesian Methods for Ecology - Rilegato

 
9780521850575: Bayesian Methods for Ecology
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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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Recensione:
"[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
Descrizione del libro:
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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  • EditoreCambridge University Press
  • Data di pubblicazione2007
  • ISBN 10 0521850576
  • ISBN 13 9780521850575
  • RilegaturaCopertina rigida
  • Numero edizione1
  • Numero di pagine310
  • Valutazione libreria

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9780521615594: Bayesian Methods for Ecology

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ISBN 10:  ISBN 13:  9780521615594
Casa editrice: Cambridge University Press, 2007
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