Traditionally, introductory statistics courses have been taught from a frequentist perspective. The recent upsurge in the use of Bayesian methods in applied statistical analysis highlights the need to expose students early on to the Bayes theorem, its advantages, and its applications. Based on the author's successful courses, Introduction to Bayesian Statistics introduces statistics from a Bayesian perspective in a way that is understandable to readers with a reasonable mathematics background. Covering most of the same ...
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Traditionally, introductory statistics courses have been taught from a frequentist perspective. The recent upsurge in the use of Bayesian methods in applied statistical analysis highlights the need to expose students early on to the Bayes theorem, its advantages, and its applications. Based on the author's successful courses, Introduction to Bayesian Statistics introduces statistics from a Bayesian perspective in a way that is understandable to readers with a reasonable mathematics background. Covering most of the same ground found in a typical statistics book-but from a Bayesian perspective-Introduction to Bayesian Statistics offers thorough, clearly-explained discussions of: Scientific data gathering, including the use of random sampling methods and randomized experiments to make inferences on cause-effect relationships The rules of probability, including joint, marginal, and conditional probability Discrete and continuous random variables Bayesian inferences for means and proportions compared with the corresponding frequentist ones The simple linear regression model analyzed in a Bayesian manner To assist in the understanding of Bayesian statistics, this introduction provides readers with exercises (with selected answers); summaries of main points from each chapter; a calculus refresher, and a summary on the use of statistical tables; and R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations (downloadable from the associated Web site)
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