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Titlebook: Applied Bayesian Statistics; With R and OpenBUGS Mary Kathryn Cowles Textbook 2013 Springer Science+Business Media New York 2013 Bayesian.

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期刊全稱Applied Bayesian Statistics
期刊簡(jiǎn)稱With R and OpenBUGS
影響因子2023Mary Kathryn Cowles
視頻videohttp://file.papertrans.cn/160/159655/159655.mp4
發(fā)行地址Practical approach is good for students of all levels.Based on over 12 years teaching Bayesian Statistics.R and OpenBUGS are essential to modern Bayesian applications
學(xué)科分類Springer Texts in Statistics
圖書(shū)封面Titlebook: Applied Bayesian Statistics; With R and OpenBUGS  Mary Kathryn Cowles Textbook 2013 Springer Science+Business Media New York 2013 Bayesian.
影響因子.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 o
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Special Considerations in Bayesian Inference, as Bayesians must worry about robustness of their inferences. One concern for both camps is that assumptions regarding the form of the likelihood can affect inference. For example, suppose that our data are scores obtained by 100 undergraduates on a calculus exam, and that we wish to use these data
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Other One-Parameter Models and Their Conjugate Priors,s a count of the number of rare events occurring per unit time, unit volume, unit distance, etc. For example, the number of new cases of rhabdomyosarcoma (a rare form of cancer) occurring in Johnson County, Iowa, each year might be represented as a Poisson random variable.
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Hierarchical Models and More on Convergence Assessment,ability distribution, and the prior, which specifies a probability distribution on the unknown parameters in the likelihood. Such a simple model is inadequate for many (probably most) real-world applications. As a result, more complex Bayesian models with additional levels are very commonly used. Su
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Model Comparison, Model Checking, and Hypothesis Testing,riteria for determining which of the candidate models is best, and whether even that model is good enough to use as the basis for inference. This chapter considers Bayesian methods of comparing models, testing hypotheses, and assessing model adequacy.
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