Ebook: Applied Bayesian Statistics: With R and OpenBUGS Examples
Author: Mary Kathryn Cowles (auth.)
- Tags: Statistical Theory and Methods
- Series: Springer Texts in Statistics 98
- Year: 2013
- Publisher: Springer-Verlag New York
- Edition: 1
- Language: English
- pdf
This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results. In addition, readers will learn to use the predominant software for Bayesian model-fitting, R and OpenBUGS. The practical approach this book takes will help students of all levels to build understanding of the concepts and procedures required to answer real questions by performing Bayesian analysis of real data. Topics covered include comparing and contrasting Bayesian and classical methods, specifying hierarchical models, and assessing Markov chain Monte Carlo output.
Kate Cowles taught Suzuki piano for many years before going to graduate school in Biostatistics. Her research areas are Bayesian and computational statistics, with application to environmental science. She is on the faculty of Statistics at The University of Iowa.
This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results. In addition, readers will learn to use the predominant software for Bayesian model-fitting, R and OpenBUGS. The practical approach this book takes will help students of all levels to build understanding of the concepts and procedures required to answer real questions by performing Bayesian analysis of real data. Topics covered include comparing and contrasting Bayesian and classical methods, specifying hierarchical models, and assessing Markov chain Monte Carlo output.
Mary Kathryn (Kate) Cowles taught Suzuki piano for many years before going to graduate school in Biostatistics. Her research areas are Bayesian and computational statistics, with application to environmental science. She is on the faculty of Statistics at The University of Iowa.
This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results. In addition, readers will learn to use the predominant software for Bayesian model-fitting, R and OpenBUGS. The practical approach this book takes will help students of all levels to build understanding of the concepts and procedures required to answer real questions by performing Bayesian analysis of real data. Topics covered include comparing and contrasting Bayesian and classical methods, specifying hierarchical models, and assessing Markov chain Monte Carlo output.
Mary Kathryn (Kate) Cowles taught Suzuki piano for many years before going to graduate school in Biostatistics. Her research areas are Bayesian and computational statistics, with application to environmental science. She is on the faculty of Statistics at The University of Iowa.
Content:
Front Matter....Pages i-xiv
What Is Bayesian Statistics?....Pages 1-11
Review of Probability....Pages 13-23
Introduction to One-Parameter Models: Estimating a Population Proportion....Pages 25-47
Inference for a Population Proportion....Pages 49-65
Special Considerations in Bayesian Inference....Pages 67-79
Other One-Parameter Models and Their Conjugate Priors....Pages 81-99
More Realism Please: Introduction to Multiparameter Models....Pages 101-110
Fitting More Complex Bayesian Models: Markov Chain Monte Carlo....Pages 111-145
Hierarchical Models and More on Convergence Assessment....Pages 147-177
Regression and Hierarchical Regression Models....Pages 179-205
Model Comparison, Model Checking, and Hypothesis Testing....Pages 207-224
Back Matter....Pages 225-232
This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results. In addition, readers will learn to use the predominant software for Bayesian model-fitting, R and OpenBUGS. The practical approach this book takes will help students of all levels to build understanding of the concepts and procedures required to answer real questions by performing Bayesian analysis of real data. Topics covered include comparing and contrasting Bayesian and classical methods, specifying hierarchical models, and assessing Markov chain Monte Carlo output.
Mary Kathryn (Kate) Cowles taught Suzuki piano for many years before going to graduate school in Biostatistics. Her research areas are Bayesian and computational statistics, with application to environmental science. She is on the faculty of Statistics at The University of Iowa.
Content:
Front Matter....Pages i-xiv
What Is Bayesian Statistics?....Pages 1-11
Review of Probability....Pages 13-23
Introduction to One-Parameter Models: Estimating a Population Proportion....Pages 25-47
Inference for a Population Proportion....Pages 49-65
Special Considerations in Bayesian Inference....Pages 67-79
Other One-Parameter Models and Their Conjugate Priors....Pages 81-99
More Realism Please: Introduction to Multiparameter Models....Pages 101-110
Fitting More Complex Bayesian Models: Markov Chain Monte Carlo....Pages 111-145
Hierarchical Models and More on Convergence Assessment....Pages 147-177
Regression and Hierarchical Regression Models....Pages 179-205
Model Comparison, Model Checking, and Hypothesis Testing....Pages 207-224
Back Matter....Pages 225-232
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