LEADER 05427nam 2200673 a 450 001 9910461430303321 005 20200520144314.0 010 $a1-283-27282-2 010 $a9786613272829 010 $a0-12-387021-6 035 $a(CKB)2670000000122785 035 $a(EBL)767268 035 $a(OCoLC)760173084 035 $a(SSID)ssj0000534331 035 $a(PQKBManifestationID)12231932 035 $a(PQKBTitleCode)TC0000534331 035 $a(PQKBWorkID)10511445 035 $a(PQKB)10942182 035 $a(MiAaPQ)EBC767268 035 $a(PPN)170603989 035 $a(Au-PeEL)EBL767268 035 $a(CaPaEBR)ebr10503269 035 $a(CaONFJC)MIL327282 035 $a(EXLCZ)992670000000122785 100 $a20110820d2012 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian population analysis using WinBUGS$b[electronic resource] $ea hierarchical perspective /$fMarc Ke?ry and Michael Schaub ; foreword by Steven R. Beissinger 205 $a1st ed. 210 $aBoston $cAcademic Press$d2012 215 $a1 online resource (555 p.) 300 $aDescription based upon print version of record. 311 $a0-12-387020-8 320 $aIncludes bibliographical references and index. 327 $aFront Cover; Bayesian Population Analysis using WinBUGS: A Hierarchical Perspective; Copyright; Dedication; Table of Contents; Foreword; Preface; Acknowledgments; 1 Introduction; 1.1 Ecology: The Study of Distribution and Abundance and of the Mechanisms Driving Their Change; 1.2 Genesis of Ecological Observations; 1.3 The Binomial Distribution as a Canonical Description of the Observation Process; 1.4 Structure and Overview of the Contents of this Book; 1.5 Benefits of Analyzing Simulated Data Sets: An Example of Bias and Precision; 1.6 Summary and Outlook; 1.7 Exercises 327 $a2 Brief Introduction to Bayesian Statistical Modeling 2.1 Introduction; 2.2 Role of Models in Science; 2.3 Statistical Models; 2.4 Frequentist and Bayesian Analysis of Statistical Models; 2.5 Bayesian Computation; 2.6 WinBUGS; 2.7 Advantages and Disadvantages of Bayesian Analyses by Posterior Sampling; 2.8 Hierarchical Models; 2.9 Summary and Outlook; 3 Introduction to the Generalized Linear Model: The Simplest Model for Count Data; 3.1 Introduction; 3.2 Statistical Models: Response = Signal + Noise; 3.2.1 The Noise Component; 3.2.2 The Signal Component 327 $a3.2.3 Bringing the Noise and the Signal Components Together: The Link Function 3.3 Poisson GLM in R and WinBUGS for Modeling Time Series of Counts; 3.3.1 Generation and Analysis of Simulated Data; 3.3.2 Analysis of Real Data Set; 3.4 Poisson GLM for Modeling Fecundity; 3.5 Binomial GLM for Modeling Bounded Counts or Proportions; 3.5.1 Generation and Analysis of Simulated Data; 3.5.2 Analysis of Real Data Set; 3.6 Summary and Outlook; 3.7 Exercises; 4 Introduction to Random Effects: Conventional Poisson GLMM for Count Data; 4.1 Introduction; 4.1.1 An Example; 4.1.2 What Are Random Effects? 327 $a4.1.3 Why Do We Treat Batches of Effects as Random?Scope of Inference; Assessment of Variability; Partitioning of Variability; Modeling of Correlations among Parameters; Accounting for All Random Processes in a Modeled System; Avoiding Pseudoreplication; Borrowing Strength; Random Effects as a Compromise between Pooling and No Pooling of Batched Effects; Combining Information; 4.1.4 Why Should We Ever Treat a Factor as Fixed?; 4.2 Accounting for Overdispersion by Random Effects-Modeling in R and WinBUGS; 4.2.1 Generation and Analysis of Simulated Data; 4.2.2 Analysis of Real Data 327 $a4.3 Mixed Models with Random Effects for Variability among Groups (Site and Year Effects)4.3.1 Generation and Analysis of Simulated Data; 4.3.2 Analysis of Real Data Set; Null or Intercept-Only Model; Fixed Site Effects; Fixed Site and Fixed Year Effects; Random Site Effects (No Year Effects); Random Site and Random Year Effects; Random Site and Random Year Effects and First-Year Fixed Observer Effect; Random Site and Random Year Effects, First-Year Fixed Observer Effect, and Overall Linear Time Trend; The Full Model; 4.4 Summary and Outlook; 4.5 Exercises 327 $a5 State-Space Models for Population Counts 330 $aBayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS, and its open-source sister OpenBugs, is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. Comprehensive and richly commented examples illustrate a wide range of models that are most relevant to the research of a modern population ecologist All WinBUGS/OpenBUGS analyses are completely integrated in software R