LEADER 02789nam 2200553 a 450 001 9910877213203321 005 20200520144314.0 010 $a1-118-20992-3 010 $a1-283-44603-0 010 $a9786613446039 010 $a0-470-56737-6 010 $a0-470-56734-1 035 $a(CKB)2550000000082633 035 $a(EBL)698546 035 $a(OCoLC)774270982 035 $a(SSID)ssj0000593933 035 $a(PQKBManifestationID)11412713 035 $a(PQKBTitleCode)TC0000593933 035 $a(PQKBWorkID)10548502 035 $a(PQKB)10375204 035 $a(MiAaPQ)EBC698546 035 $a(EXLCZ)992550000000082633 100 $a20090625d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aUnderstanding computational Bayesian statistics /$fWilliam M. Bolstad 210 $aHoboken, N.J. $cWiley$d2010 215 $a1 online resource (334 p.) 225 1 $aWiley series in computational statistics 300 $a"A John Wiley & Sons, Inc., publication." 311 $a0-470-04609-0 320 $aIncludes bibliographical references and index. 327 $aIntroduction 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. 330 $aA hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustra 410 0$aWiley series in computational statistics. 606 $aBayesian statistical decision theory$xData processing 615 0$aBayesian statistical decision theory$xData processing. 676 $a519.5/42 700 $aBolstad$b William M.$f1943-$0321712 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910877213203321 996 $aUnderstanding computational Bayesian statistics$94189371 997 $aUNINA