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[null,"1.","Record Nr.",null,"UNINA9910961920503321"]
[null,null,"Autore",null,"Kéry Marc"]
[null,null,"Titolo",null,"Bayesian population analysis using WinBUGS : a hierarchical perspective / / Marc Kéry and Michael Schaub ; foreword by Steven R. Beissinger"]
[null,null,"Pubbl/distr/stampa",null,"Boston, : Academic Press, 2012"]
[null,null,"ISBN",null,"9786613272829"]
[{"$nulls":4},"9781283272827"]
[{"$nulls":4},"1283272822"]
[{"$nulls":4},"9780123870216"]
[{"$nulls":4},"0123870216"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (555 p.)"]
[null,null,"Disciplina",null,"577.880285"]
[{"$nulls":4},"577.880727"]
[null,null,"Soggetti",null,"Population biology - Data processing"]
[{"$nulls":4},"R (Computer program language)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Front 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"]
[{"$nulls":4},"2 Brief Introduction to Bayesian Statistical Modeling 2.1 Introduction; 2.2 Role of Models in Science; 2.3 Statistical Models; 2.4 Frequentist and Bayesian Analysis of Statistical Models; 2.5 Bayesian Computation; 2.6 WinBUGS; 2.7 Advantages and Disadvantages of Bayesian Analyses by Posterior Sampling; 2.8 Hierarchical Models; 2.9 Summary and Outlook; 3 Introduction to the Generalized Linear Model: The Simplest Model for Count Data; 3.1 Introduction; 3.2 Statistical Models: Response = Signal + Noise; 3.2.1 The Noise Component; 3.2.2 The Signal Component"]
[{"$nulls":4},"3.2.3 Bringing the Noise and the Signal Components Together: The Link Function 3.3 Poisson GLM in R and WinBUGS for Modeling Time Series "]
[{"$nulls":4},"of Counts; 3.3.1 Generation and Analysis of Simulated Data; 3.3.2 Analysis of Real Data Set; 3.4 Poisson GLM for Modeling Fecundity; 3.5 Binomial GLM for Modeling Bounded Counts or Proportions; 3.5.1 Generation and Analysis of Simulated Data; 3.5.2 Analysis of Real Data Set; 3.6 Summary and Outlook; 3.7 Exercises; 4 Introduction to Random Effects: Conventional Poisson GLMM for Count Data; 4.1 Introduction; 4.1.1 An Example; 4.1.2 What Are Random Effects?"]
[{"$nulls":4},"4.1.3 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"]
[{"$nulls":4},"4.3 Mixed Models with Random Effects for Variability among Groups (Site and Year Effects)4.3.1 Generation and Analysis of Simulated Data; 4.3.2 Analysis of Real Data Set; Null or Intercept-Only Model; Fixed Site Effects; Fixed Site and Fixed Year Effects; Random Site Effects (No Year Effects); Random Site and Random Year Effects; Random Site and Random Year Effects and First-Year Fixed Observer Effect; Random Site and Random Year Effects, First-Year Fixed Observer Effect, and Overall Linear Time Trend; The Full Model; 4.4 Summary and Outlook; 4.5 Exercises"]
[{"$nulls":4},"5 State-Space Models for Population Counts"]
[null,null,null,"Sommario/riassunto","Bayesian 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<li"]