001452412 000__ 03197cam\a22004337a\4500
001452412 001__ 1452412
001452412 003__ OCoLC
001452412 005__ 20230310003354.0
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001452412 008__ 230131s2022\\\\si\a\\\\ob\\\\000\0\eng\d
001452412 020__ $$a9789811947551$$q(electronic bk.)
001452412 020__ $$a9811947554$$q(electronic bk.)
001452412 020__ $$z9811947546
001452412 020__ $$z9789811947544
001452412 0247_ $$a10.1007/978-981-19-4755-1$$2doi
001452412 035__ $$aSP(OCoLC)1366057834
001452412 040__ $$aYDX$$beng$$cYDX$$dGW5XE
001452412 049__ $$aISEA
001452412 050_4 $$aQA279.5
001452412 08204 $$a519.5/42$$223/eng/20230201
001452412 1001_ $$aMatsuura, Kentaro,$$eauthor.
001452412 24510 $$aBayesian statistical modeling with Stan, R, and Python/$$cKentaro Matsuura.
001452412 260__ $$aSingapore :$$bSpringer,$$c[2022]
001452412 300__ $$a1 online resource :$$billustrations
001452412 504__ $$aIncludes bibliographical references.
001452412 5050_ $$aIntroduction -- Introduction of Stan -- Essential Components and Techniques for Experts -- Advanced Topics for Real-world Data.
001452412 506__ $$aAccess limited to authorized users.
001452412 520__ $$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.
001452412 650_0 $$aBayesian statistical decision theory.
001452412 650_0 $$aBayesian statistical decision theory$$xData processing.
001452412 655_0 $$aElectronic books.
001452412 77608 $$iPrint version: $$z9811947546$$z9789811947544$$w(OCoLC)1330404078
001452412 852__ $$bebk
001452412 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-4755-1$$zOnline Access$$91397441.1
001452412 909CO $$ooai:library.usi.edu:1452412$$pGLOBAL_SET
001452412 980__ $$aBIB
001452412 980__ $$aEBOOK
001452412 982__ $$aEbook
001452412 983__ $$aOnline
001452412 994__ $$a92$$bISE