Bayesian Data Analysis, 3rd Edition | Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin

Winner Of The 2016 De Groot Prize From The International Society For Bayesian Analysis Now In Its Third Edition, This Classic Book Is Widely Considered The Leading Text On Bayesian Methods, Lauded For Its Accessible, Practical Approach To Analyzing Data And Solving Research Problems. Bayesian Data Analysis, Third Edition Continues To Take An Applied Approach To Analysis Using UpToDate Bayesian Methods. The AuthorsAll Leaders In The Statistics CommunityIntroduce Basic Concepts From A DataAnalytic Perspective Before Presenting Advanced Methods. Throughout The Text, Numerous Worked Examples Drawn From Real Applications And Research Emphasize The Use Of Bayesian Inference In Practice. New To The Third Edition Four New Chapters On Nonparametric Modeling Coverage Of Weakly Informative Priors And BoundaryAvoiding Priors Updated Discussion Of CrossValidation And Predictive Information Criteria Improved Convergence Monitoring And Effective Sample Size Calculations For Iterative Simulation Presentations Of Hamiltonian Monte Carlo, Variational Bayes, And Expectation Propagation New And Revised Software Code The Book Can Be Used In Three Different Ways. For Undergraduate Students, It Introduces Bayesian Inference Starting From First Principles. For Graduate Students, The Text Presents Effective Current Approaches To Bayesian Modeling And Computation In Statistics And Related Fields. For Researchers, It Provides An Assortment Of Bayesian Methods In Applied Statistics. Additional Materials, Including Data Sets Used In The Examples, Solutions To Selected Exercises, And Software Instructions, Are Available On The BookS Web Page.