Praise for the First Edition "I cannot think of a better book for teachers of introductory statistics who want a readable and pedagogically sound text to introduce Bayesian statistics." Statistics in Medical Research "[This book] is written in a lucid conversational style, which is so rare in mathematical writings. It does an excellent job of presenting Bayesian statistics as a perfectly reasonable approach to elementary problems in statistics." STATS: The Magazine for Students of Statistics, American Statistical Association "Bolstad offers clear explanations of every concept and method making…mehr
Praise for the First Edition "I cannot think of a better book for teachers of introductory statistics who want a readable and pedagogically sound text to introduce Bayesian statistics." Statistics in Medical Research "[This book] is written in a lucid conversational style, which is so rare in mathematical writings. It does an excellent job of presenting Bayesian statistics as a perfectly reasonable approach to elementary problems in statistics." STATS: The Magazine for Students of Statistics, American Statistical Association "Bolstad offers clear explanations of every concept and method making the book accessible and valuable to undergraduate and graduate students alike." Journal of Applied Statistics The use of Bayesian methods in applied statistical analysis has become increasingly popular, yet most introductory statistics texts continue to only present the subject using frequentist methods. Introduction to Bayesian Statistics, Second Edition focuses on Bayesian methods that can be used for inference, and it also addresses how these methods compare favorably with frequentist alternatives. Teaching statistics from the Bayesian perspective allows for direct probability statements about parameters, and this approach is now more relevant than ever due to computer programs that allow practitioners to work on problems that contain many parameters. This book uniquely covers the topics typically found in an introductory statistics book but from a Bayesian perspective giving readers an advantage as they enter fields where statistics is used. This Second Edition provides: Extended coverage of Poisson and Gamma distributions Two new chapters on Bayesian inference for Poisson observations and Bayesian inference for the standard deviation for normal observations A twenty five percent increase in exercises with selected answers at the end of the book A calculus refresher appendix and a summary on the use of statistical tables New computer exercises that use R functions and Minitab® macros for Bayesian analysis and Monte Carlo simulations Introduction to Bayesian Statistics, Second Edition is an invaluable textbook for advanced undergraduate and graduate level statistics courses as well as a practical reference for statisticians who require a working knowledge of Bayesian statistics.
William M. Bolstad, PhD, is Senior Lecturer in the Department of Statistics at The University of Waikato, New Zealand. He holds degrees from the University of Missouri, Stanford University, and The University of Waikato.?Dr. Bolstad's research interests include Bayesian statistics, MCMC methods, recursive estimation techniques, multiprocess dynamic time series models, and forecasting.
Inhaltsangabe
Preface.
Preface to First Edition.
1. Introduction to Statistical Science.
2. Scientific Data Gathering.
Monte Carlo Exercises.
3. Displaying and Summarizing Data.
Exercises.
4. Logic, Probability, and Uncertainty.
Exercises.
5. Discrete Random Variables.
Exercises.
6. Bayesian Inference for Discrete Random Variables.
Exercises.
Computer Exercises.
7. Continuous Random Variables.
Exercises.
8. Bayesian Inference for Binomial Proportion.
Exercises.
Computer Exercises.
9. Comparing Bayesian and Frequentist Inferences for Proportion.
Exercises.
Monte Carlo Exercises.
10. Bayesian Inference for Poisson.
Exercises.
Computer Exercises.
11. Bayesian Inference for Normal Mean.
Exercises.
Computer Exercises.
12. Comparing Bayesian and Frequentist Inferences for Mean.
Exercises.
13. Bayesian Inference for Difference between Means.
Exercises.
14. Bayesian Inference for Simple Linear Regression.
6. Bayesian Inference for Discrete Random Variables.
Exercises.
Computer Exercises.
7. Continuous Random Variables.
Exercises.
8. Bayesian Inference for Binomial Proportion.
Exercises.
Computer Exercises.
9. Comparing Bayesian and Frequentist Inferences for Proportion.
Exercises.
Monte Carlo Exercises.
10. Bayesian Inference for Poisson.
Exercises.
Computer Exercises.
11. Bayesian Inference for Normal Mean.
Exercises.
Computer Exercises.
12. Comparing Bayesian and Frequentist Inferences for Mean.
Exercises.
13. Bayesian Inference for Difference between Means.
Exercises.
14. Bayesian Inference for Simple Linear Regression.
Exercises.
Computer Exercises.
15. Bayesian Inference for Standard Deviation.
Exercises.
Computer Exercises.
16. Robust Bayesian Methods.
Exercises.
Computer Exercises.
A. Introduction to Calculus.
B. Use of Statistical Tables.
C. Using the Included Minitab Macros.
D. Using the Included R Functions.
E. Answers to Selected Exercises.
Bibliography.
References.
Rezensionen
"Like the first edition, this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods." ( Technometrics, November 2008) "Highly recommended. Upper division undergraduates; graduate students; professionals." ( CHOICE , April 2008)
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