There has been a dramatic growth in the development and application of Bayesian inferential methods. This book introduces Bayesian modeling by the use of computation using the R language. The new ...edition contains changes in the R code illustrations.
Bayesian statistics has gained great momentum since the computational developments of the 1990s. Gradually, advances in Bayesian methodology and software have made Bayesian techniques much more ...accessible to applied statisticians and, in turn, have potentially transformed Bayesian education at the undergraduate level. This article provides an overview of the various options for implementing Bayesian computational methods motivated to achieve particular learning outcomes. For each computational method, we propose activities and exercises, and discuss each method's pedagogical advantages and disadvantages based on our experience in the classroom. The goal is to present guidance on the choice of computation for the instructors who are introducing Bayesian methods in their undergraduate statistics curriculum.
Supplementary materials
for this article are available online.
Carl Morris 1938–2023 was well-known for his pioneering research in Bayesian multiparameter inference and prediction. Morris was also known for his development of statistical thinking and methodology ...in sports. This paper provides an overview of Morris’ contributions in sports. This includes Morris’ experience in sports as a youth, summaries of some of Morris’ best-known contributions using sports data, his influence working with students, and some of Morris’ thinking about the interplay of statistics and sports.
R by Example Albert, Jim; Rizzo, Maria
2012, 2011
eBook
Odprti dostop
R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in ...the context of interesting applications with real data. The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, this book is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data and this book is intended to be a useful resource for learning how to implement these procedures in R.
R by Example Albert, Jim; Rizzo, Maria
11/2011
eBook
The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, this book is written ...for users who have covered at least the equivalent of undergraduate level calculus-based courses in statistics.
Standard measures of batting performance such as batting average and on-base percentage can be decomposed into component rates such as strikeout rates and home run rates. The likelihood of hitting ...data for a group of players can be expressed as a product of likelihoods of the component probabilities and this motivates the use of Bayesian random effects models to estimate the groups of component rates. By combining the separate component rates, the aggregate predictions of batting performance for subsequent seasons improve upon standard shrinkage methods. This “separate and aggregate” approach is also illustrated for estimating on-base probabilities and fielding independent pitching (FIP) abilities of pitchers.
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