LEADER 00728nam0 2200265 450 001 9910280647803321 005 20180724110219.0 010 $a88-244-5118-7 100 $a20180724d1983---- km y0itay50 ba 101 0 $aita 102 $aIT 105 $ay n 001yy 200 1 $a<>quattro codici$ee leggi complementari$fa cura di Franco Izzo ... [et al.] 210 $aNapoli$cEsselibri-Simone$dstampa 1983 215 $a2363 p.$d19 cm 225 1 $aEdizioni Simone$v511 702 1$aIzzo,$bFranco 710 01$aItalia$0423419 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910280647803321 952 $a6-72$bs.i.$fDSPCP 959 $aDSPCP 996 $aQuattro codici$9699812 997 $aUNINA LEADER 04270nam 22006855 450 001 996508571103316 005 20240216190322.0 010 $a9789811947551$b(electronic bk.) 010 $z9789811947544 024 7 $a10.1007/978-981-19-4755-1 035 $a(MiAaPQ)EBC7186265 035 $a(Au-PeEL)EBL7186265 035 $a(CKB)26050263700041 035 $a(DE-He213)978-981-19-4755-1 035 $a(PPN)267810954 035 $a(EXLCZ)9926050263700041 100 $a20230124d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBayesian Statistical Modeling with Stan, R, and Python$b[electronic resource] /$fby Kentaro Matsuura 205 $a1st ed. 2022. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022. 215 $a1 online resource (395 pages) 311 08$aPrint version: Matsuura, Kentaro Bayesian Statistical Modeling with Stan, R, and Python Singapore : Springer,c2023 9789811947544 327 $aIntroduction -- Introduction of Stan -- Essential Components and Techniques for Experts -- Advanced Topics for Real-world Data. 330 $aThis book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language. The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes modeling more engineering than art. The second part discusses the use of Stan, CmdStanR, and CmdStanPy from the very beginning to basic regression analyses. The third part then introduces a number of probability distributions, nonlinear models, and hierarchical (multilevel) models, which are essential to mastering statistical modeling. It also describes a wide range of frequently used modeling techniques, such as censoring, outliers, missing data, speed-up, and parameter constraints, and discusses how to lead convergence of MCMC. Lastly, the fourth part examines advanced topics for real-world data: longitudinal data analysis, state space models, spatial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 lines. Using numerous easy-to-understand examples, the book explains key concepts, which continue to be useful when using future versions of Stan and when using other statistical modeling tools. The examples do not require domain knowledge and can be generalized to many fields. The book presents full explanations of code and math formulas, enabling readers to extend models for their own problems. All the code and data are on GitHub. 606 $aMathematical statistics$xData processing 606 $aStatistics 606 $aBiometry 606 $aSocial sciences$xStatistical methods 606 $aStatistics and Computing 606 $aStatistical Theory and Methods 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aBiostatistics 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 606 $aEstadística bayesiana$2thub 606 $aProcessament de dades$2thub 608 $aLlibres electrònics$2thub 615 0$aMathematical statistics$xData processing. 615 0$aStatistics. 615 0$aBiometry. 615 0$aSocial sciences$xStatistical methods. 615 14$aStatistics and Computing. 615 24$aStatistical Theory and Methods. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aBiostatistics. 615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 615 7$aEstadística bayesiana 615 7$aProcessament de dades 676 $a519.542 700 $aMatsuura$b Kentaro$01275248 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a996508571103316 996 $aBayesian Statistical Modeling with Stan, R, and Python$93004751 997 $aUNISA