國立高雄大學圖資館 |

Language: English

Back

WAIC and WBIC with R Stan100 exercises for building logic /

Record Type:	Electronic resources : Monograph/item
Title/Author:	WAIC and WBIC with R Stanby Joe Suzuki.
Reminder of title:	100 exercises for building logic /
Author:	Suzuki, Joe.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	xii, 239 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Bayesian statistical decision theory.
Online resource:	https://doi.org/10.1007/978-981-99-3838-4
ISBN:	9789819938384$q(electronic bk.)

WAIC and WBIC with R Stan100 exercises for building logic /
Suzuki, Joe.

WAIC and WBIC with R Stan100 exercises for building logic /[electronic resource] :by Joe Suzuki. - Singapore :Springer Nature Singapore :2023. - xii, 239 p. :ill., digital ;24 cm.

Over view of Watanabe's Bayes -- Introduction to Watanabe Bayesian Theory -- MCMC and Stan -- Mathematical Preparation -- Regular Statistical Models -- Information Criteria -- Algebraic Geometry -- The Essence of WAOIC -- WBIC and Its Application to Machine Learning.

Master the art of machine learning and data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the Watanabe Bayesian information criterion. This book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in R and Stan. Whether you're a data scientist looking to refine your model selection process or a researcher who wants to explore the latest developments in Bayesian statistics, this accessible guide will give you a firm grasp of Watanabe Bayesian Theory. The key features of this indispensable book include: A clear and self-contained writing style, ensuring ease of understanding for readers at various levels of expertise. 100 carefully selected exercises accompanied by solutions in the main text, enabling readers to effectively gauge their progress and comprehension. A comprehensive guide to Sumio Watanabe's groundbreaking Bayes theory, demystifying a subject once considered too challenging even for seasoned statisticians. Detailed source programs and Stan codes that will enhance readers' grasp of the mathematical concepts presented. A streamlined approach to algebraic geometry topics in Chapter 6, making Bayes theory more accessible and less daunting. Embark on your machine learning and data science journey with this essential textbook and unlock the full potential of WAIC and WBIC today!

ISBN: 9789819938384$q(electronic bk.)

Standard No.: 10.1007/978-981-99-3838-4doiSubjects--Topical Terms:

182005
Bayesian statistical decision theory.

LC Class. No.: QA279.5 / .S89 2023

Dewey Class. No.: 519.542

WAIC and WBIC with R Stan100 exercises for building logic /
LDR:02880nmm a2200325 a 4500 001 651893
003 DE-He213
005 20231024170252.0
006 m d
007 cr nn 008maaau
008 240327s2023 si s 0 eng d
020 $a 9789819938384$q(electronic bk.)
020 $a 9789819938377$q(paper)
024 7 $a 10.1007/978-981-99-3838-4 $2 doi
035 $a 978-981-99-3838-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA279.5 $b .S89 2023
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 519.542 $2 23
090 $a QA279.5 $b .S968 2023
100 1 $a Suzuki, Joe. $3 732855
245 1 0 $a WAIC and WBIC with R Stan $h [electronic resource] : $b 100 exercises for building logic / $c by Joe Suzuki.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xii, 239 p. : $b ill., digital ; $c 24 cm.
505 0 $a Over view of Watanabe's Bayes -- Introduction to Watanabe Bayesian Theory -- MCMC and Stan -- Mathematical Preparation -- Regular Statistical Models -- Information Criteria -- Algebraic Geometry -- The Essence of WAOIC -- WBIC and Its Application to Machine Learning.
520 $a Master the art of machine learning and data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the Watanabe Bayesian information criterion. This book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in R and Stan. Whether you're a data scientist looking to refine your model selection process or a researcher who wants to explore the latest developments in Bayesian statistics, this accessible guide will give you a firm grasp of Watanabe Bayesian Theory. The key features of this indispensable book include: A clear and self-contained writing style, ensuring ease of understanding for readers at various levels of expertise. 100 carefully selected exercises accompanied by solutions in the main text, enabling readers to effectively gauge their progress and comprehension. A comprehensive guide to Sumio Watanabe's groundbreaking Bayes theory, demystifying a subject once considered too challenging even for seasoned statisticians. Detailed source programs and Stan codes that will enhance readers' grasp of the mathematical concepts presented. A streamlined approach to algebraic geometry topics in Chapter 6, making Bayes theory more accessible and less daunting. Embark on your machine learning and data science journey with this essential textbook and unlock the full potential of WAIC and WBIC today!
650 0 $a Bayesian statistical decision theory. $3 182005
650 0 $a R (Computer program language) $3 210846
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Statistical Learning. $3 921460
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Data Science. $3 913495
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-99-3838-4
950 $a Computer Science (SpringerNature-11645)