02840nam 2200565 a 450 991083068660332120230725054537.01-118-20992-31-283-44603-097866134460390-470-56737-60-470-56734-1(CKB)2550000000082633(EBL)698546(OCoLC)774270982(SSID)ssj0000593933(PQKBManifestationID)11412713(PQKBTitleCode)TC0000593933(PQKBWorkID)10548502(PQKB)10375204(MiAaPQ)EBC698546(EXLCZ)99255000000008263320090625d2010 uy 0engur|n|---|||||txtccrUnderstanding computational Bayesian statistics[electronic resource] /William M. BolstadHoboken, N.J. Wiley20101 online resource (334 p.)Wiley series in computational statistics"A John Wiley & Sons, Inc., publication."0-470-04609-0 Includes bibliographical references and index.Introduction to Bayesian statistics -- Monte Carlo sampling from the posterior -- Bayesian inference -- Bayesian statistics using conjugate priors -- Markov chains -- Markov chain Monte Carlo sampling from the posterior -- Statistical inference from a Markov chain Monte Carlo sample -- Logistic regression -- Poisson regression and proportional hazards model -- Gibbs sampling and hierarchical models -- Going forward with Markov chain Monte Carlo -- Appendix A: Using the included Minitab macros -- Appendix B: Using the included R functions.A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustraWiley series in computational statistics.Bayesian statistical decision theoryData processingBayesian statistical decision theoryData processing.519.5/42519.542Bolstad William M.1943-321712MiAaPQMiAaPQMiAaPQBOOK9910830686603321Understanding computational Bayesian statistics3929173UNINA