{"product_id":"bayesian-statistical-methods-9780815378648","title":"Bayesian Statistical Methods","description":"\u003cp\u003e • Author(s): Brian J. Reich\u003cbr\u003e • Publisher: Taylor \u0026amp; Francis\u003cbr\u003e • Publisher Imprint: CRC Press\u003cbr\u003e • BISAC: Probability \u0026amp; Statistics - General\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBayesian Statistical Methods\u003c\/strong\u003e provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures.\u003c\/p\u003e\u003cp\u003eIn addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: \u003c\/p\u003e\u003cul\u003e \u003cp\u003e \u003c\/p\u003e \u003cli\u003eAdvice on selecting prior distributions\u003c\/li\u003e \u003cp\u003e \u003c\/p\u003e \u003cli\u003eComputational methods including Markov chain Monte Carlo (MCMC) \u003c\/li\u003e \u003cp\u003e \u003c\/p\u003e \u003cli\u003eModel-comparison and goodness-of-fit measures, including sensitivity to priors\u003c\/li\u003e \u003cp\u003e \u003c\/p\u003e \u003cli\u003eFrequentist properties of Bayesian methods\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003eCase studies covering advanced topics illustrate the flexibility of the Bayesian approach: \u003c\/p\u003e\u003cul\u003e \u003cp\u003e \u003c\/p\u003e \u003cli\u003eSemiparametric regression \u003c\/li\u003e \u003cp\u003e \u003c\/p\u003e \u003cli\u003eHandling of missing data using predictive distributions\u003c\/li\u003e \u003cp\u003e \u003c\/p\u003e \u003cli\u003ePriors for high-dimensional regression models\u003c\/li\u003e \u003cp\u003e \u003c\/p\u003e \u003cli\u003eComputational techniques for large datasets\u003c\/li\u003e \u003cp\u003e \u003c\/p\u003e \u003cli\u003eSpatial data analysis\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003eThe advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book's website.\u003c\/p\u003e\u003cp\u003eBrian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the \u003ci\u003eJournal of Agricultural, Biological, and Environmental Statistics \u003c\/i\u003eand was awarded the LeRoy \u0026amp; Elva Martin Teaching Award.\u003c\/p\u003e\u003cp\u003eSujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.\u003c\/p\u003e","brand":"Taylor \u0026 Francis","offers":[{"title":"Hardcover","offer_id":45239451189399,"sku":"9780815378648","price":11117.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9780815378648.webp?v=1769222622","url":"https:\/\/atlanticbooks.com\/products\/bayesian-statistical-methods-9780815378648","provider":"Atlantic Books","version":"1.0","type":"link"}