LEADER 01389nam 2200409 450 001 990000735850203316 010 $a88-7078-152-2 035 $a0073585 035 $aUSA010073585 035 $a(ALEPH)000073585USA01 035 $a0073585 100 $a20011112d1993----km-y0itay0103----ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aPensiero organizzativo e azione manageriale$egli stili del pensiero che orientano la conoscenza dell'organizzazione e legittimano l'azione del manager$fCesare Kaneklin, Giuliana Aretino 210 $aMilano$cR. Cortina$d1993 215 $aXV, 123 p$d22 cm 225 2 $aIndividuo, gruppo, organizzazione 410 $12001$aIndividuo, gruppo, organizzazione 606 0 $aAziende$xOrganizzazione 606 0 $aDirigenti aziendali 676 $a658.5 700 1$aKANEKLIN,$bCesare$0477749 701 1$aARETINO,$bGiuliana$0382053 801 0$aIT$bsalbc$gISBD 912 $a990000735850203316 951 $aXV C COLL. 6/5$b155705 LM$cXV C COLL. 951 $aXV C COLL. 6/5 BIS$b155706 LM$cXV C COLL. 951 $aXV C COLL. 6/5 A$b155707 LM$cXV C COLL. 959 $aBK 969 $aUMA 979 $aPATTY$b90$c20011112$lUSA01$h1641 979 $c20020403$lUSA01$h1721 979 $aPATRY$b90$c20040406$lUSA01$h1651 996 $aPensiero organizzativo e azione manageriale$9964144 997 $aUNISA LEADER 02874nam 2200577 a 450 001 9910139717303321 005 20210104162918.0 010 $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$b[electronic resource] /$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 608 $aElectronic books. 615 0$aBayesian statistical decision theory$xData processing. 676 $a519.5/42 676 $a519.542 700 $aBolstad$b William M.$f1943-$0321712 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139717303321 996 $aUnderstanding computational Bayesian statistics$92275706 997 $aUNINA