國立虎尾科技大學 |

Bayesian statistical modeling with Stan, R, and Python

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Bayesian statistical modeling with Stan, R, and Python/ by Kentaro Matsuura.
作者:	Matsuura, Kentaro.
出版者:	Singapore :Springer Nature Singapore : : 2022.,
面頁冊數:	xix, 385 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Bayesian statistical decision theory. -
電子資源:	https://doi.org/10.1007/978-981-19-4755-1
ISBN:	9789811947551

Bayesian statistical modeling with Stan, R, and Python
Matsuura, Kentaro.

Bayesian statistical modeling with Stan, R, and Python[electronic resource] /by Kentaro Matsuura. - Singapore :Springer Nature Singapore :2022. - xix, 385 p. :ill., digital ;24 cm.

Introduction -- Introduction of Stan -- Essential Components and Techniques for Experts -- Advanced Topics for Real-world Data.

This 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.

ISBN: 9789811947551

Standard No.: 10.1007/978-981-19-4755-1doiSubjects--Topical Terms:

527671
Bayesian statistical decision theory.

LC Class. No.: QA279.5 / .M38 2022

Dewey Class. No.: 519.542

Bayesian statistical modeling with Stan, R, and Python
LDR:02808nam a2200349 a 4500 001 1105459
003 DE-He213
005 20230124045058.0
006 m d
007 cr nn 008maaau
008 231013s2022 si s 0 eng d
020 $a 9789811947551 $q (electronic bk.)
020 $a 9789811947544 $q (paper)
024 7 $a 10.1007/978-981-19-4755-1 $2 doi
035 $a 978-981-19-4755-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA279.5 $b .M38 2022
072 7 $a PBT $2 bicssc
072 7 $a UFM $2 bicssc
072 7 $a COM077000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a UFM $2 thema
082 0 4 $a 519.542 $2 23
090 $a QA279.5 $b .M434 2022
100 1 $a Matsuura, Kentaro. $3 1414498
245 1 0 $a Bayesian statistical modeling with Stan, R, and Python $h [electronic resource] / $c by Kentaro Matsuura.
260 $a Singapore : $c 2022. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a xix, 385 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Introduction of Stan -- Essential Components and Techniques for Experts -- Advanced Topics for Real-world Data.
520 $a This 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.
650 0 $a Bayesian statistical decision theory. $3 527671
650 0 $a Bayesian statistical decision theory $x Data processing. $3 564780
650 1 4 $a Statistics and Computing. $3 1366004
650 2 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Statistics in Business, Management, Economics, Finance, Insurance. $3 1366003
650 2 4 $a Biostatistics. $3 783654
650 2 4 $a Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. $3 1366322
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-19-4755-1
950 $a Mathematics and Statistics (SpringerNature-11649)