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

Case studies in applied Bayesian dat...

Mengersen, Kerrie L.

Linked to FindBook

Google Book

Amazon

博客來

Case studies in applied Bayesian data science = CIRM Jean-Morlet Chair, Fall 2018 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Case studies in applied Bayesian data science/ edited by Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert.
Reminder of title:	CIRM Jean-Morlet Chair, Fall 2018 /
other author:	Mengersen, Kerrie L.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	vi, 420 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Bayesian statistical decision theory. -
Online resource:	https://doi.org/10.1007/978-3-030-42553-1
ISBN:	9783030425531

Case studies in applied Bayesian data science = CIRM Jean-Morlet Chair, Fall 2018 /
Case studies in applied Bayesian data scienceCIRM Jean-Morlet Chair, Fall 2018 /[electronic resource] :edited by Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert. - Cham :Springer International Publishing :2020. - vi, 420 p. :ill., digital ;24 cm. - Lecture notes in mathematics,22590075-8434 ;. - Lecture notes in mathematics ;2259..

Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor. The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields.While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution. The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration.

ISBN: 9783030425531

Standard No.: 10.1007/978-3-030-42553-1doiSubjects--Topical Terms:

551404
Bayesian statistical decision theory.

LC Class. No.: QA279.5 / .C374 2020

Dewey Class. No.: 519.542

Case studies in applied Bayesian data science = CIRM Jean-Morlet Chair, Fall 2018 /
LDR:02376nmm a2200325 a 4500 001 2255358
003 DE-He213
005 20200527235406.0
006 m d
007 cr nn 008maaau
008 220419s2020 sz s 0 eng d
020 $a 9783030425531 $q (electronic bk.)
020 $a 9783030425524 $q (paper)
024 7 $a 10.1007/978-3-030-42553-1 $2 doi
035 $a 978-3-030-42553-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA279.5 $b .C374 2020
072 7 $a PBTB $2 bicssc
072 7 $a MAT029010 $2 bisacsh
072 7 $a PBTB $2 thema
082 0 4 $a 519.542 $2 23
090 $a QA279.5 $b .C337 2020
245 0 0 $a Case studies in applied Bayesian data science $h [electronic resource] : $b CIRM Jean-Morlet Chair, Fall 2018 / $c edited by Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a vi, 420 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in mathematics, $x 0075-8434 ; $v 2259
520 $a Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor. The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields.While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution. The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration.
650 0 $a Bayesian statistical decision theory. $3 551404
650 1 4 $a Bayesian Inference. $3 3386929
650 2 4 $a Probability Theory and Stochastic Processes. $3 891080
650 2 4 $a Applied Statistics. $3 3300946
700 1 $a Mengersen, Kerrie L. $3 1308662
700 1 $a Pudlo, Pierre. $3 3524884
700 1 $a Robert, Christian P. $3 2056091
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Lecture notes in mathematics ; $v 2259. $3 3524885
856 4 0 $u https://doi.org/10.1007/978-3-030-42553-1
950 $a Mathematics and Statistics (Springer-11649)