Statistical Rethinking: A Bayesian Course With Examples In R And Stan

Product Description

Statistical Rethinking A Bayesian Course with Examples in R & Stan builds readers' knowledge of & confidence in statistical modeling Reflecting the need for even minor programming in today's model-based statistics the book pushes readers to perform step-by-step calculations that are usually automated This unique computational approach ensures that readers understand enough of the details to make reasonable choices & interpretations in their own modeling work The text presents generalized linear multilevel models from a Bayesian perspective relying on a simple logical interpretation of Bayesian probability & maximum entropy It covers from the basics of regression to multilevel models The author also discusses measurement error missing data & Gaussian process models for spatial & network autocorrelation By using complete R code examples throughout this book provides a practical foundation for performing statistical inference Designed for both Ph D students & seasoned professionals in the natural & social sciences it prepares them for more advanced or specialized statistical modeling Web Resource The book is accompanied by an R package (rethinking) that is available on the author's website & Git Hub The two core functions (map & map 2stan) of this package allow a variety of statistical models to be constructed from standard model formulas