REBIUN - ODA

Descripción del título

Case studies in Bayesian st...

Case studies in Bayesian statistical modelling and analysis

Alston, Clair

Mengersen, Kerrie L.

Pettitt, Anthony (Anthony N.)

John Wiley & Sons Inc. 2012

Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how

Ver

Electronic books

Monografía

monografia Rebiun21538094 https://catalogo.rebiun.org/rebiun/record/Rebiun21538094 m o d | cr -n--------- 120815s2012 enk ob 001 0 eng d 2012034365 1-118-39447-X 1-283-65634-5 1-118-39449-6 UPVA 996879965203706 UAM 991007687950104211 UPM 991005522080204212 UCAR 991007817687004213 CBUC 991000732729206712 CBUC 991010341559106709 UPCT u284587 MiAaPQ MiAaPQ MiAaPQ eng 519.542 Case studies in Bayesian statistical modelling and analysis electronic resource] edited by Clair Alston, Kerrie Mengersen, and Anthony Pettitt Chichester, West Sussex John Wiley & Sons Inc. 2012 Chichester, West Sussex Chichester, West Sussex John Wiley & Sons Inc. 1 online resource (499 p.) 1 online resource (499 p.) Text txt computer c online resource cr Wiley Series in Probability and Statistics Description based upon print version of record Includes bibliographical references and index Case Studies in Bayesian Statistical Modelling and Analysis; Contents; Preface; List of contributors; 1 Introduction; 1.1 Introduction; 1.2 Overview; 1.3 Further reading; 1.3.1 Bayesian theory and methodology; 1.3.2 Bayesian methodology; 1.3.3 Bayesian computation; 1.3.4 Bayesian software; 1.3.5 Applications; References; 2 Introduction to MCMC; 2.1 Introduction; 2.2 Gibbs sampling; 2.2.1 Example: Bivariate normal; 2.2.2 Example: Change-point model; 2.3 Metropolis-Hastings algorithms; 2.3.1 Example: Component-wise MH or MH within Gibbs; 2.3.2 Extensions to basic MCMC; 2.3.3 Adaptive MCMC 2.3.4 Doubly intractable problems2.4 Approximate Bayesian computation; 2.5 Reversible jump MCMC; 2.6 MCMC for some further applications; References; 3 Priors: Silent or active partners of Bayesian inference?; 3.1 Priors in the very beginning; 3.1.1 Priors as a basis for learning; 3.1.2 Priors and philosophy; 3.1.3 Prior chronology; 3.1.4 Pooling prior information; 3.2 Methodology I: Priors defined by mathematical criteria; 3.2.1 Conjugate priors; 3.2.2 Impropriety and hierarchical priors; 3.2.3 Zellner's g-prior for regression models; 3.2.4 Objective priors 3.3 Methodology II: Modelling informative priors3.3.1 Informative modelling approaches; 3.3.2 Elicitation of distributions; 3.4 Case studies; 3.4.1 Normal likelihood: Time to submit research dissertations; 3.4.2 Binomial likelihood: Surveillance for exotic plant pests; 3.4.3 Mixture model likelihood: Bioregionalization; 3.4.4 Logistic regression likelihood: Mapping species distribution via habitat models; 3.5 Discussion; 3.5.1 Limitations; 3.5.2 Finding out about the problem; 3.5.3 Prior formulation; 3.5.4 Communication; 3.5.5 Conclusion; Acknowledgements; References 4 Bayesian analysis of the normal linear regression model4.1 Introduction; 4.2 Case studies; 4.2.1 Case study 1: Boston housing data set; 4.2.2 Case study 5.1 Introduction5.2 Case study: Updating a known risk-adjustment model for local use; 5.3 Models and methods; 5.4 Data analysis and results; 5.4.1 Updating using the training data; 5.4.2 Updating the model yearly; 5.5 Discussion; References; 6 A Bayesian regression model with variable selection for genome-wide association studies; 6.1 Introduction; 6.2 Case study: Case-control of Type 1 diabetes; 6.3 Case study: GENICA; 6.4 Models and methods; 6.4.1 Main effect models; 6.4.2 Main effects and interactions; 6.5 Data analysis and results; 6.5.1 WTCCC TID; 6.5.2 GENICA; 6.6 Discussion Acknowledgements Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how English Bayesian statistical decision theory Statistical decision Electronic books Alston, Clair Mengersen, Kerrie L. Pettitt, Anthony Anthony N.) 1-118-39432-1 1-119-94182-2 Wiley Series in Probability and Statistics

• Localizaciones: 24
• Préstamo interbibliotecario

• Exportar
  • Dublin Core
  • Google scholar
  • RIS
  • RefWorks
• Favorito
• Compartir

Información Sólo puedes introducir una dirección de email ×

Introduce un email para compartir el título

Email Pon el email de la persona con la que deseas compartir este contenido

Error .alert.alert-info

Cerrar Compartir por email

Información Haz clic en el cuadro de texto para seleccionar el contenido y copiarlo al portapapeles

Enlace permanente https://catalogo.rebiun.org/rebiun/record/Rebiun21538094

Cerrar

monografia Rebiun21538094 https://catalogo.rebiun.org/rebiun/record/Rebiun21538094 m o d | cr -n--------- 120815s2012 enk ob 001 0 eng d 2012034365 1-118-39447-X 1-283-65634-5 1-118-39449-6 UPVA 996879965203706 UAM 991007687950104211 UPM 991005522080204212 UCAR 991007817687004213 CBUC 991000732729206712 CBUC 991010341559106709 UPCT u284587 MiAaPQ MiAaPQ MiAaPQ eng 519.542 Case studies in Bayesian statistical modelling and analysis electronic resource] edited by Clair Alston, Kerrie Mengersen, and Anthony Pettitt Chichester, West Sussex John Wiley & Sons Inc. 2012 Chichester, West Sussex Chichester, West Sussex John Wiley & Sons Inc. 1 online resource (499 p.) 1 online resource (499 p.) Text txt computer c online resource cr Wiley Series in Probability and Statistics Description based upon print version of record Includes bibliographical references and index Case Studies in Bayesian Statistical Modelling and Analysis; Contents; Preface; List of contributors; 1 Introduction; 1.1 Introduction; 1.2 Overview; 1.3 Further reading; 1.3.1 Bayesian theory and methodology; 1.3.2 Bayesian methodology; 1.3.3 Bayesian computation; 1.3.4 Bayesian software; 1.3.5 Applications; References; 2 Introduction to MCMC; 2.1 Introduction; 2.2 Gibbs sampling; 2.2.1 Example: Bivariate normal; 2.2.2 Example: Change-point model; 2.3 Metropolis-Hastings algorithms; 2.3.1 Example: Component-wise MH or MH within Gibbs; 2.3.2 Extensions to basic MCMC; 2.3.3 Adaptive MCMC 2.3.4 Doubly intractable problems2.4 Approximate Bayesian computation; 2.5 Reversible jump MCMC; 2.6 MCMC for some further applications; References; 3 Priors: Silent or active partners of Bayesian inference?; 3.1 Priors in the very beginning; 3.1.1 Priors as a basis for learning; 3.1.2 Priors and philosophy; 3.1.3 Prior chronology; 3.1.4 Pooling prior information; 3.2 Methodology I: Priors defined by mathematical criteria; 3.2.1 Conjugate priors; 3.2.2 Impropriety and hierarchical priors; 3.2.3 Zellner's g-prior for regression models; 3.2.4 Objective priors 3.3 Methodology II: Modelling informative priors3.3.1 Informative modelling approaches; 3.3.2 Elicitation of distributions; 3.4 Case studies; 3.4.1 Normal likelihood: Time to submit research dissertations; 3.4.2 Binomial likelihood: Surveillance for exotic plant pests; 3.4.3 Mixture model likelihood: Bioregionalization; 3.4.4 Logistic regression likelihood: Mapping species distribution via habitat models; 3.5 Discussion; 3.5.1 Limitations; 3.5.2 Finding out about the problem; 3.5.3 Prior formulation; 3.5.4 Communication; 3.5.5 Conclusion; Acknowledgements; References 4 Bayesian analysis of the normal linear regression model4.1 Introduction; 4.2 Case studies; 4.2.1 Case study 1: Boston housing data set; 4.2.2 Case study 5.1 Introduction5.2 Case study: Updating a known risk-adjustment model for local use; 5.3 Models and methods; 5.4 Data analysis and results; 5.4.1 Updating using the training data; 5.4.2 Updating the model yearly; 5.5 Discussion; References; 6 A Bayesian regression model with variable selection for genome-wide association studies; 6.1 Introduction; 6.2 Case study: Case-control of Type 1 diabetes; 6.3 Case study: GENICA; 6.4 Models and methods; 6.4.1 Main effect models; 6.4.2 Main effects and interactions; 6.5 Data analysis and results; 6.5.1 WTCCC TID; 6.5.2 GENICA; 6.6 Discussion Acknowledgements Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how English Bayesian statistical decision theory Statistical decision Electronic books Alston, Clair Mengersen, Kerrie L. Pettitt, Anthony Anthony N.) 1-118-39432-1 1-119-94182-2 Wiley Series in Probability and Statistics