Introduction to Hierarchical Bayesian Modeling for Ecological Data

Introduction to Hierarchical Bayesian Modeling for Ecological Data

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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 more

Product details

  • Hardback | 427 pages
  • 160.02 x 236.22 x 25.4mm | 635.03g
  • Taylor & Francis Ltd
  • Chapman & Hall/CRC
  • United States
  • English
  • New.
  • 45 Tables, black and white; 143 Illustrations, black and white
  • 1584889195
  • 9781584889199
  • 904,873

Review quote

"This book is a welcome addition to the Bayesian literature. It is well written and amply illustrates Bayesian methods with practical applications in fisheries management. The programs for data analyses are available on the book's website, allowing users to get their `hands dirty' and in the process really understand the model construction and the software."- Subhash R. Lele, Ecology, 95(1), 2014 "The book is well written and easy to read, and the material presented deserves a greater exposure in taught statistics courses. I thoroughly recommend the book and believe that the statistical techniques and their application to quantitative fisheries science could ideally complement a short undergraduate course in applied statistics."-Carl M. O'Brien, International Statistical Review (2013), 81show more

About Eric Parent

Eric Parent is head of the Research Laboratory for Risk Management in Environmental Sciences (Team MORSE) and a professor in applied statistics and probabilistic modeling for environmental engineering at the National Institute for Rural Engineering, Water and Forest Management (ENGREF/AgroParisTech) in Paris, France. Dr. Parent's research encompasses Bayesian theory and applications, with special emphasis on environmental systems modeling. Etienne Rivot is a researcher in the Fisheries Ecology Laboratory at Agrocampus Ouest in Rennes, France. Dr. Rivot's research focuses on the application of Bayesian statistical modeling for the analysis of ecological data, inference, and more

Table of contents

I Basic Blocks of Bayesian Modeling Bayesian Hierarchical Models in Statistical Ecology Challenges for statistical ecologyConditional reasoning, graphs and hierarchical modelsBayesian inferences on hierarchical modelsWhat can be found in this book? The Beta-Binomial ModelFrom a scientific question to a Bayesian analysis What is modeling?Think conditionally and make a graphical representation Inference is the reverse way of thinking Expertise matters Encoding prior knowledge The conjugate Beta pdf Bayesian inference as statistical learningBayesian inference as a statistical tool for prediction Asymptotic behavior of the beta-binomial model The beta-binomial model with WinBUGSFurther references The Basic Normal ModelSalmon farm's pollutants and juvenile growth A Normal model for the fish length Normal-gamma as conjugate models to encode expertiseInference by recourse to conjugate propertyBibliographical notesFurther material Working with More Than One Beta-Binomial Element Capture-mark-recapture analysisSuccessive removal analysisTesting a new tag for tunaFurther references Combining Various Sources of Information Motivating example Stochastic model for salmon behaviorInference with WinBUGS ResultsDiscussion and conclusions The Normal Linear Model The decrease of Thiof abundance in Senegal Linear model theoryA linear model for Thiof abundanceFurther reading Nonlinear Models for Stock-Recruitment Analysis Stock-recruitment motivating example Searching for a SR model Which parameters?Changing the error term from lognormal to gamma From Ricker to Beverton and Holt Model choice with informative prior Conclusions and perspectives Getting beyond Regression Models Logistic and probit regressionsOrdered probit modelDiscussion II More Elaborate Hierarchical Structures HBM I: Borrowing Strength from Similar Units Introduction HBM for capture-mark-recapture dataHierarchical stock-recruitment analysisFurther Bayesian comments on exchangeability HBM II: Piling up Simple Layers HBM for successive removal data with habitat and yearElectrofishing with successive removals HBM III: State-Space Modeling Introduction State-space modeling of a biomass production modelState-space modeling of Atlantic salmon life cycle modelA tool of choice for the ecological detective Decision and Planning Summary Introduction The See-Selune river network Salmon life cycle dynamicsLong-term behavior: Collapse or equilibrium? Management reference points Management rules and implementation error Economic modelResultsDiscussion Appendix A: The Normal and Linear Normal ModelAppendix B: Computing Marginal LikelihoodsAppendix C: The Baseball Players' Historical ExampleAppendix D: More on Ricker Stock-Recruitment Bibliography Indexshow more