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Bayesian population analysis using WinBUGS [[electronic resource] ] : a hierarchical perspective / / Marc Kéry and Michael Schaub ; foreword by Steven R. Beissinger
| Bayesian population analysis using WinBUGS [[electronic resource] ] : a hierarchical perspective / / Marc Kéry and Michael Schaub ; foreword by Steven R. Beissinger |
| Autore | Kéry Marc |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Boston, : Academic Press, 2012 |
| Descrizione fisica | 1 online resource (555 p.) |
| Disciplina |
577.880285
577.880727 |
| Altri autori (Persone) |
SchaubMichael
BeissingerSteven R |
| Soggetto topico | Population biology - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-27282-2
9786613272829 0-12-387021-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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
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 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 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? 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 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 5 State-Space Models for Population Counts |
| Record Nr. | UNINA-9910461430303321 |
Kéry Marc
|
||
| Boston, : Academic Press, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian population analysis using WinBUGS [[electronic resource] ] : a hierarchical perspective / / Marc Kéry and Michael Schaub ; foreword by Steven R. Beissinger
| Bayesian population analysis using WinBUGS [[electronic resource] ] : a hierarchical perspective / / Marc Kéry and Michael Schaub ; foreword by Steven R. Beissinger |
| Autore | Kéry Marc |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Boston, : Academic Press, 2012 |
| Descrizione fisica | 1 online resource (555 p.) |
| Disciplina |
577.880285
577.880727 |
| Altri autori (Persone) |
SchaubMichael
BeissingerSteven R |
| Soggetto topico |
Population biology - Data processing
R (Computer program language) |
| ISBN |
1-283-27282-2
9786613272829 0-12-387021-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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
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 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 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? 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 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 5 State-Space Models for Population Counts |
| Record Nr. | UNINA-9910789718803321 |
|
Kéry Marc
|
||
| Boston, : Academic Press, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian population analysis using WinBUGS : a hierarchical perspective / / Marc Kéry and Michael Schaub ; foreword by Steven R. Beissinger
| Bayesian population analysis using WinBUGS : a hierarchical perspective / / Marc Kéry and Michael Schaub ; foreword by Steven R. Beissinger |
| Autore | Kéry Marc |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Boston, : Academic Press, 2012 |
| Descrizione fisica | 1 online resource (555 p.) |
| Disciplina |
577.880285
577.880727 |
| Altri autori (Persone) |
SchaubMichael
BeissingerSteven R |
| Soggetto topico |
Population biology - Data processing
R (Computer program language) |
| ISBN |
1-283-27282-2
9786613272829 0-12-387021-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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
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 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 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? 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 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 5 State-Space Models for Population Counts |
| Record Nr. | UNINA-9910808147803321 |
|
Kéry Marc
|
||
| Boston, : Academic Press, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||