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Title: INTRODUCTION TO HIERARCHICAL BAYESIAN MODELING FOR ECOLOGICAL DATA
By: Eric Parent, Etienne Rivot, Marcel F. Neuts
Format: Hardback

List price: £145.00
Our price: £130.50
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ISBN 10: 1584889195
ISBN 13: 9781584889199
Availability: Usually dispatched within 1-3 weeks.
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Publisher: TAYLOR & FRANCIS LTD
Pub. date: 27 August, 2012
Series: Chapman & Hall/CRC Applied Environmental Statistics 8
Pages: 427
Synopsis: Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models. The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors' website. This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions.It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.
Illustrations: 143 black & white illustrations, 45 black & white tables
Publication: US
Imprint: Chapman & Hall/CRC
Returns: Returnable
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