東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Bayesian statistical decision theory" ]

切換: 標籤 | MARC模式 | ISBD

New frontiers in Bayesian statistics...

Bayesian Young Statisticians Meeting (2021 :)

FindBook

Google Book

Amazon

博客來

New frontiers in Bayesian statistics = BAYSM 2021, online, September 1-3 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	New frontiers in Bayesian statistics/ edited by Raffaele Argiento, Federico Camerlenghi, Sally Paganin.
其他題名:	BAYSM 2021, online, September 1-3 /
其他題名:	BAYSM 2021
其他作者:	Argiento, Raffaele.
團體作者:	Bayesian Young Statisticians Meeting
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xi, 117 p. :ill. (some color), digital ;24 cm.
內容註:	1 Andrej Srakar, Approximate Bayesian algorithm for tensor robust principal component analysis -- 2 Yuanqi Chu, Xueping Hu, Keming Yu, Bayesian Quantile Regression for Big Data Analysis -- 3 Peter Strong, Alys McAlphine, Jim Smith, Towards A Bayesian Analysis of Migration Pathways using Chain Event Graphs of Agent Based Models -- 4 Giorgos Tzoumerkas, Dimitris Fouskakis, Power-Expected-Posterior Methodology with Baseline Shrinkage Priors -- 5 Mica Teo, Sara Wade, Bayesian nonparametric scalar-on-image regression via Potts-Gibbs random partition models -- 6 Alessandro Colombi, Block Structured Graph Priors in Gaussian Graphical Models -- 7 Jessica Pavani, Paula Moraga, A Bayesian joint spatio-temporal model for multiple mosquito-borne diseases -- 8 Ivan Gutierrez, Luis Gutierrez, Danilo Alvare, A Bayesian nonparametric test for cross-group differences relative to a control -- 9 Francesco Gaffi, Antonio Lijoi, Igor Pruenster, Specification of the base measure of nonparametric priors via random means -- 10 Matteo Pedone, Raffaele Argiento, Francesco Claudio Stingo, Bayesian Nonparametric Predictive Modeling for Personalized Treatment Selection -- 11 Gabriel Calvo, carmen armero, Virgilio Gómez-Rubio, Guido Mazzinari, Bayesian growth curve model for studying the intra-abdominal volume during pneumoperitoneum for laparoscopic surgery.
Contained By:	Springer Nature eBook
標題:	Bayesian statistical decision theory - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-031-16427-9
ISBN:	9783031164279

New frontiers in Bayesian statistics = BAYSM 2021, online, September 1-3 /
New frontiers in Bayesian statisticsBAYSM 2021, online, September 1-3 /[electronic resource] :BAYSM 2021edited by Raffaele Argiento, Federico Camerlenghi, Sally Paganin. - Cham :Springer International Publishing :2022. - xi, 117 p. :ill. (some color), digital ;24 cm. - Springer proceedings in mathematics & statistics,v. 4052194-1017 ;. - Springer proceedings in mathematics & statistics ;v. 405..

1 Andrej Srakar, Approximate Bayesian algorithm for tensor robust principal component analysis -- 2 Yuanqi Chu, Xueping Hu, Keming Yu, Bayesian Quantile Regression for Big Data Analysis -- 3 Peter Strong, Alys McAlphine, Jim Smith, Towards A Bayesian Analysis of Migration Pathways using Chain Event Graphs of Agent Based Models -- 4 Giorgos Tzoumerkas, Dimitris Fouskakis, Power-Expected-Posterior Methodology with Baseline Shrinkage Priors -- 5 Mica Teo, Sara Wade, Bayesian nonparametric scalar-on-image regression via Potts-Gibbs random partition models -- 6 Alessandro Colombi, Block Structured Graph Priors in Gaussian Graphical Models -- 7 Jessica Pavani, Paula Moraga, A Bayesian joint spatio-temporal model for multiple mosquito-borne diseases -- 8 Ivan Gutierrez, Luis Gutierrez, Danilo Alvare, A Bayesian nonparametric test for cross-group differences relative to a control -- 9 Francesco Gaffi, Antonio Lijoi, Igor Pruenster, Specification of the base measure of nonparametric priors via random means -- 10 Matteo Pedone, Raffaele Argiento, Francesco Claudio Stingo, Bayesian Nonparametric Predictive Modeling for Personalized Treatment Selection -- 11 Gabriel Calvo, carmen armero, Virgilio Gómez-Rubio, Guido Mazzinari, Bayesian growth curve model for studying the intra-abdominal volume during pneumoperitoneum for laparoscopic surgery.

This book presents a selection of peer-reviewed contributions to the fifth Bayesian Young Statisticians Meeting, BaYSM 2021, held virtually due to the COVID-19 pandemic on 1-3 September 2021. Despite all the challenges of an online conference, the meeting provided a valuable opportunity for early career researchers, including MSc students, PhD students, and postdocs to connect with the broader Bayesian community. The proceedings highlight many different topics in Bayesian statistics, presenting promising methodological approaches to address important challenges in a variety of applications. The book is intended for a broad audience of people interested in statistics, and provides a series of stimulating contributions on theoretical, methodological, and computational aspects of Bayesian statistics.

ISBN: 9783031164279

Standard No.: 10.1007/978-3-031-16427-9doiSubjects--Topical Terms:

612456
Bayesian statistical decision theory
--Congresses.

LC Class. No.: QA279.5

Dewey Class. No.: 519.542

New frontiers in Bayesian statistics = BAYSM 2021, online, September 1-3 /
LDR:03326nmm a2200349 a 4500 001 2305817
003 DE-He213
005 20221126192832.0
006 m d
007 cr nn 008maaau
008 230409s2022 sz s 0 eng d
020 $a 9783031164279 $q (electronic bk.)
020 $a 9783031164262 $q (paper)
024 7 $a 10.1007/978-3-031-16427-9 $2 doi
035 $a 978-3-031-16427-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA279.5
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.542 $2 23
090 $a QA279.5 $b .B357 2021
111 2 $a Bayesian Young Statisticians Meeting $n (5th : $d 2021 : $c Online) $3 3609270
245 1 0 $a New frontiers in Bayesian statistics $h [electronic resource] : $b BAYSM 2021, online, September 1-3 / $c edited by Raffaele Argiento, Federico Camerlenghi, Sally Paganin.
246 3 $a BAYSM 2021
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xi, 117 p. : $b ill. (some color), digital ; $c 24 cm.
490 1 $a Springer proceedings in mathematics & statistics, $x 2194-1017 ; $v v. 405
505 0 $a 1 Andrej Srakar, Approximate Bayesian algorithm for tensor robust principal component analysis -- 2 Yuanqi Chu, Xueping Hu, Keming Yu, Bayesian Quantile Regression for Big Data Analysis -- 3 Peter Strong, Alys McAlphine, Jim Smith, Towards A Bayesian Analysis of Migration Pathways using Chain Event Graphs of Agent Based Models -- 4 Giorgos Tzoumerkas, Dimitris Fouskakis, Power-Expected-Posterior Methodology with Baseline Shrinkage Priors -- 5 Mica Teo, Sara Wade, Bayesian nonparametric scalar-on-image regression via Potts-Gibbs random partition models -- 6 Alessandro Colombi, Block Structured Graph Priors in Gaussian Graphical Models -- 7 Jessica Pavani, Paula Moraga, A Bayesian joint spatio-temporal model for multiple mosquito-borne diseases -- 8 Ivan Gutierrez, Luis Gutierrez, Danilo Alvare, A Bayesian nonparametric test for cross-group differences relative to a control -- 9 Francesco Gaffi, Antonio Lijoi, Igor Pruenster, Specification of the base measure of nonparametric priors via random means -- 10 Matteo Pedone, Raffaele Argiento, Francesco Claudio Stingo, Bayesian Nonparametric Predictive Modeling for Personalized Treatment Selection -- 11 Gabriel Calvo, carmen armero, Virgilio Gómez-Rubio, Guido Mazzinari, Bayesian growth curve model for studying the intra-abdominal volume during pneumoperitoneum for laparoscopic surgery.
520 $a This book presents a selection of peer-reviewed contributions to the fifth Bayesian Young Statisticians Meeting, BaYSM 2021, held virtually due to the COVID-19 pandemic on 1-3 September 2021. Despite all the challenges of an online conference, the meeting provided a valuable opportunity for early career researchers, including MSc students, PhD students, and postdocs to connect with the broader Bayesian community. The proceedings highlight many different topics in Bayesian statistics, presenting promising methodological approaches to address important challenges in a variety of applications. The book is intended for a broad audience of people interested in statistics, and provides a series of stimulating contributions on theoretical, methodological, and computational aspects of Bayesian statistics.
650 0 $a Bayesian statistical decision theory $v Congresses. $3 612456
700 1 $a Argiento, Raffaele. $3 3236699
700 1 $a Camerlenghi, Federico. $3 3609271
700 1 $a Paganin, Sally. $3 3609272
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer proceedings in mathematics & statistics ; $v v. 405. $3 3609273
856 4 0 $u https://doi.org/10.1007/978-3-031-16427-9
950 $a Mathematics and Statistics (SpringerNature-11649)