
Bayesian Analysis for Population Ecology by Ruth King
Emphasizing model choice and model averaging, this book presents Bayesian methods for analyzing complex ecological data. It provides a basic introduction to Bayesian methods that assumes no prior knowledge. It includes descriptions of methods that deal with covariate data and covers techniques at the forefront of research.-
Statistical and Econometric Methods for Transportation Data Analysis
- Bayesian Computational Methods in Statistical Signal Processing
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Age-Period-Cohort Analysis
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Measurement Error
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Clinical Trials in Oncology
- Compositional Data Analysis in Practice
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Bayesian Analysis of Capture-Recapture Data with Hidden Markov Models
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Survival Analysis with Interval-Censored Data
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Statistics for Fission Track Analysis
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Analysis of Capture-Recapture Data
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Missing Data Analysis in Practice
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Power Analysis of Trials with Multilevel Data
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Statistical Methods in Epilepsy
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Applied Directional Statistics
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An Invariant Approach to Statistical Analysis of Shapes
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Bayesian Disease Mapping
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Modern Directional Statistics
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Statistical Methods in Psychiatry and Related Fields
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Statistics of Medical Imaging
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Capture-Recapture Methods for the Social and Medical Sciences
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Multiple Imputation of Missing Data in Practice
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Bayesian Modeling of Spatio-Temporal Data with R
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Clinical Trials in Oncology, Third Edition
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Correspondence Analysis in Practice
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Introduction to Computational Biology
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Model-based Geostatistics for Global Public Health
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Design of Experiments for Generalized Linear Models
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Statistics for Biological Networks
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Parameter Redundancy and Identifiability
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Model-Based Monitoring and Statistical Control
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Flexible Imputation of Missing Data, Second Edition
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The Data Book
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Mendelian Randomization
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Statistical Analysis of Questionnaires
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Design and Analysis of Quality of Life Studies in Clinical Trials
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Statistical Detection and Surveillance of Geographic Clusters
"Although the book draws largely from questions and issues relevant to wildlife management, it serves as a useful guide for individuals outside the fieldOverall, Bayesian Analysis for Population Ecology makes a great addition to a practicing ecologist’s statistical bookshelf. As the author’s state, the volume can also serve as a textbook and form a strong base for teaching an upper-division or graduate-level course in Bayesian statistics."
—Bret D. Elderd, The Quarterly Review of Biology, March 2013
"The primary strengths of this book are the authors’ extensive practical experience in applying Bayesian methods and the advanced material on model selection and multimodel inference, particularly via reversible jump Markov chain Monte Carlo. This would be a valuable reference for those already familiar with core Bayesian methods, and who are looking to learn more about ecological statistics or to implement these methods for complex ecological data. … Several fully worked examples taken mostly from the authors’ own research are presented in each chapter, and these go a long way in helping to unravel some of the art of Bayesian inference. The material is well presented and will be informative both to statisticians seeking an introduction to ecological modeling and to ecologists wishing to learn about Bayesian inference."
—Simon Bonner, Biometrics, 2011
"The book is divided into three parts. … Part 1 contains a wealth of material on aspects of such data, models analysis as well as the [historical] evolution of the subject. Part 2 is a good, self-contained introduction to Bayesian analysis … Part 3 is a collection of interesting special topics in ecological applications. … The authors write very well and illustrate with good examples. Both the technical and nontechnical discussions are good."
—International Statistical Review (2011), 79, 1
"… the book under review will be of value for quantitative ecologists. The authors offer good practical advice on the implementation of MCMC and model selection, using data types familiar to wildlife ecologists. The text includes exercises at the end of each chapter in Sections 1 and 2; these and the primers on programs R and WinBUGS are attractive features. The authors have had a leading role promoting Reversible Jump MCMC as a tool for multimodel inference in wildlife and ecological applications, and their book continues this work."
—The American Statistician, February 2011, Vol. 65, No. 1
"… a solid introduction to Bayesian modeling. … The authors have produced a text that is not only of good use to those who are analyzing population ecological data, but to anyone desiring a good overview of Bayesian modeling in general. The examples are interesting and do not hinder those not in the discipline of population ecology from understanding the explanation of the statistical principles being discussed. I recommend the book for a graduate-level course on Bayesian modeling, as well as any course related to the Bayesian modeling of population ecological data. The reader is not expected to have a prior knowledge of Bayesian modeling, nor is there an assumption that readers are familiar with R or WinBUGS. …"
—Journal of Statistical Software, August 2010, Volume 36
Ruth King is a reader in statistics at the University of St. Andrews and a former EPSRC post-doctoral Research Fellow.
Byron J.T. Morgan is a professor of applied statistics at the University of Kent and co-director of the EPSRC National Centre for Statistical Ecology.
Olivier Gimenez is a research scientist in biostatistics at CNRS and a former Marie Curie research fellow.
Stephen P. Brooks is director of research at ATASS Ltd and a former professor of statistics at the University of Cambridge and EPSRC Advanced Fellow.
| SKU | Unavailable |
| ISBN 13 | 9781439811870 |
| ISBN 10 | 1439811873 |
| Title | Bayesian Analysis for Population Ecology |
| Author | Ruth King |
| Series | Chapman And Hall Crc Interdisciplinary Statistics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Taylor & Francis Inc |
| Year published | 2009-10-30 |
| Number of pages | 456 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |

































