Bayesian Analysis For Population Ecology

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    Bayesian Analysis for Population Ecology
    By: Ruth King
    Publisher:
    Chapman and Hall/CRC
    Print ISBN: 9781439811870, 1439811873
    eText ISBN: 9781439811887, 1439811881
    Edition: 1st
    Pages: 456
    Copyright year: 2009
    Format: PDF
    Available from $ 23.18 USD
    SKU 9781439811887R90
    Novel Statistical Tools for Conserving and Managing Populations By gathering information on key demographic parameters, scientists can often predict how populations will develop in the future and relate these parameters to external influences, such as global warming. Because of their ability to easily incorporate random effects, fit state-space models, evaluate posterior model probabilities, and deal with missing data, modern Bayesian methods have become important in this area of statistical inference and forecasting. Emphasising model choice and model averaging, Bayesian Analysis for Population Ecology presents up-to-date methods for analysing complex ecological data. Leaders in the statistical ecology field, the authors apply the theory to a wide range of actual case studies and illustrate the methods using WinBUGS and R. The computer programs and full details of the data sets are available on the book’s website. The first part of the book focuses on models and their corresponding likelihood functions. The authors examine classical methods of inference for estimating model parameters, including maximum-likelihood estimates of parameters using numerical optimisation algorithms. After building this foundation, the authors develop the Bayesian approach for fitting models to data. They also compare Bayesian and traditional approaches to model fitting and inference. Exploring challenging problems in population ecology, this book shows how to use the latest Bayesian methods to analyse data. It enables readers to apply the methods to their own problems with confidence.
    Additional ISBNs
    9786612902765, 9781439811887, 9781282902763, 6612902760, 1439811881, 1282902768
     

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