東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

WAIC and WBIC with Python Stan = 100...

Suzuki, Joe.

Linked to FindBook

Google Book

Amazon

博客來

WAIC and WBIC with Python Stan = 100 exercises for building logic /

Record Type:	Electronic resources : Monograph/item
Title/Author:	WAIC and WBIC with Python Stan/ by Joe Suzuki.
Reminder of title:	100 exercises for building logic /
Author:	Suzuki, Joe.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	xii, 242 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Bayesian statistical decision theory. -
Online resource:	https://doi.org/10.1007/978-981-99-3841-4
ISBN:	9789819938414

WAIC and WBIC with Python Stan = 100 exercises for building logic /
Suzuki, Joe.

WAIC and WBIC with Python Stan100 exercises for building logic /[electronic resource] :by Joe Suzuki. - Singapore :Springer Nature Singapore :2023. - xii, 242 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 Python 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: 9789819938414

Standard No.: 10.1007/978-981-99-3841-4doiSubjects--Topical Terms:

551404
Bayesian statistical decision theory.

LC Class. No.: QA279.5

Dewey Class. No.: 519.542

WAIC and WBIC with Python Stan = 100 exercises for building logic /
LDR:02900nmm a2200325 a 4500 001 2390040
003 DE-He213
005 20231220115336.0
006 m d
007 cr nn 008maaau
008 250916s2023 si s 0 eng d
020 $a 9789819938414 $q (electronic bk.)
020 $a 9789819938407 $q (paper)
024 7 $a 10.1007/978-981-99-3841-4 $2 doi
035 $a 978-981-99-3841-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA279.5
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $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 2165769
245 1 0 $a WAIC and WBIC with Python 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, 242 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 Python 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 551404
650 0 $a Logic, Symbolic and mathematical. $3 532051
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Statistical Learning. $3 3597795
650 2 4 $a Data Science. $3 3538937
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-99-3841-4
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)