國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Prior processes and their applicatio...

Phadia, Eswar G.

Prior processes and their applicationsnonparametric bayesian estimation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Prior processes and their applicationsby Eswar G. Phadia.
其他題名:	nonparametric bayesian estimation /
作者:	Phadia, Eswar G.
出版者:	Cham :Springer International Publishing :2016.
面頁冊數:	xvii, 327 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Bayesian statistical theory.
電子資源:	http://dx.doi.org/10.1007/978-3-319-32789-1
ISBN:	9783319327891$q(electronic bk.)

Prior processes and their applicationsnonparametric bayesian estimation /
Phadia, Eswar G.

Prior processes and their applicationsnonparametric bayesian estimation /[electronic resource] :by Eswar G. Phadia. - 2nd ed. - Cham :Springer International Publishing :2016. - xvii, 327 p. :ill., digital ;24 cm. - Springer series in statistics,0172-7397. - Springer series in statistics..

Prior Processes -- Inference Based on Complete Data -- Inference Based on Incomplete Data.

This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for dealing with Bayesian approach to solving selected nonparametric inference problems. This revised edition has been substantially expanded to reflect the current interest in this area. After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process. It subsequently discusses various neutral to right type processes, including gamma and extended gamma, beta and beta-Stacy processes, and then describes the Chinese Restaurant, Indian Buffet and infinite gamma-Poisson processes, which prove to be very useful in areas such as machine learning, information retrieval and featural modeling. Tailfree and Polya tree and their extensions form a separate chapter, while the last two chapters present the Bayesian solutions to certain estimation problems pertaining to the distribution function and its functional based on complete data as well as right censored data. Because of the conjugacy property of some of these processes, most solutions are presented in closed form. However, the current interest in modeling and treating large-scale and complex data also poses a problem - the posterior distribution, which is essential to Bayesian analysis, is invariably not in a closed form, making it necessary to resort to simulation. Accordingly, the book also introduces several computational procedures, such as the Gibbs sampler, Blocked Gibbs sampler and slice sampling, highlighting essential steps of algorithms while discussing specific models. In addition, it features crucial steps of proofs and derivations, explains the relationships between different processes and provides further clarifications to promote a deeper understanding. Lastly, it includes a comprehensive list of references, equipping readers to explore further on their own.

ISBN: 9783319327891$q(electronic bk.)

Standard No.: 10.1007/978-3-319-32789-1doiSubjects--Topical Terms:

753657
Bayesian statistical theory.

LC Class. No.: QA278.8 / .P43 2016

Dewey Class. No.: 519.542

Prior processes and their applicationsnonparametric bayesian estimation /
LDR:03306nmm a2200337 a 4500 001 493094
003 DE-He213
005 20160727163555.0
006 m d
007 cr nn 008maaau
008 170220s2016 gw s 0 eng d
020 $a 9783319327891$q(electronic bk.)
020 $a 9783319327884$q(paper)
024 7 $a 10.1007/978-3-319-32789-1 $2 doi
035 $a 978-3-319-32789-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278.8 $b .P43 2016
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.542 $2 23
090 $a QA278.8 $b .P532 2016
100 1 $a Phadia, Eswar G. $3 753656
245 1 0 $a Prior processes and their applications $h [electronic resource] : $b nonparametric bayesian estimation / $c by Eswar G. Phadia.
250 $a 2nd ed.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xvii, 327 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in statistics, $x 0172-7397
505 0 $a Prior Processes -- Inference Based on Complete Data -- Inference Based on Incomplete Data.
520 $a This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for dealing with Bayesian approach to solving selected nonparametric inference problems. This revised edition has been substantially expanded to reflect the current interest in this area. After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process. It subsequently discusses various neutral to right type processes, including gamma and extended gamma, beta and beta-Stacy processes, and then describes the Chinese Restaurant, Indian Buffet and infinite gamma-Poisson processes, which prove to be very useful in areas such as machine learning, information retrieval and featural modeling. Tailfree and Polya tree and their extensions form a separate chapter, while the last two chapters present the Bayesian solutions to certain estimation problems pertaining to the distribution function and its functional based on complete data as well as right censored data. Because of the conjugacy property of some of these processes, most solutions are presented in closed form. However, the current interest in modeling and treating large-scale and complex data also poses a problem - the posterior distribution, which is essential to Bayesian analysis, is invariably not in a closed form, making it necessary to resort to simulation. Accordingly, the book also introduces several computational procedures, such as the Gibbs sampler, Blocked Gibbs sampler and slice sampling, highlighting essential steps of algorithms while discussing specific models. In addition, it features crucial steps of proofs and derivations, explains the relationships between different processes and provides further clarifications to promote a deeper understanding. Lastly, it includes a comprehensive list of references, equipping readers to explore further on their own.
650 0 $a Bayesian statistical theory. $3 753657
650 0 $a Bayesian statistical decision theory. $3 182005
650 0 $a Nonparametric statistics. $3 182700
650 1 4 $a Statistics. $3 182057
650 2 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 274067
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 348605
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Springer series in statistics. $3 280826
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-32789-1
950 $a Mathematics and Statistics (Springer-11649)