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

切換: 標籤 | MARC模式 | ISBD

Bayesian data analysis /

Gelman, Andrew.

FindBook

Google Book

Amazon

博客來

Bayesian data analysis /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Bayesian data analysis // Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin.
作者:	Gelman, Andrew.
出版者:	Boca Raton :CRC Press, : 2014.,
面頁冊數:	xiv, 661 p. :ill. ;27 cm.
標題:	Bayesian statistical decision theory. -
ISBN:	9781439840955 (hbk.) :

Bayesian data analysis /
Gelman, Andrew.

Bayesian data analysis /Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin. - 3rd ed. - Boca Raton :CRC Press,2014. - xiv, 661 p. :ill. ;27 cm. - Chapman & Hall/CRC texts in statistical science..

Includes bibliographical references (pages 607-639) and indexes.

"Preface This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics. The mathematics used in our book is basic probability and statistics, elementary calculus, and linear algebra. A review of probability notation is given in Chapter 1 along with a more detailed list of topics assumed to have been studied. The practical orientation of the book means that the reader's previous experience in probability, statistics, and linear algebra should ideally have included strong computational components. To write an introductory text alone would leave many readers with only a taste of the conceptual elements but no guidance for venturing into genuine practical applications, beyond those where Bayesian methods agree essentially with standard non-Bayesian analyses. On the other hand, we feel it would be a mistake to present the advanced methods without first introducing the basic concepts from our data-analytic perspective. Furthermore, due to the nature of applied statistics, a text on current Bayesian methodology would be incomplete without a variety of worked examples drawn from real applications. To avoid cluttering the main narrative, there are bibliographic notes at the end of each chapter and references at the end of the book"--

ISBN: 9781439840955 (hbk.) :GBP44.99

LCCN: 2013039507Subjects--Topical Terms:

551404
Bayesian statistical decision theory.

LC Class. No.: QA279.5 / .G45 2014

Dewey Class. No.: 519.5/42

Bayesian data analysis /
LDR:02512cam a2200229 a 4500 001 1917917
008 140516s2014 flua b 001 0 eng
010 $a 2013039507
020 $a 9781439840955 (hbk.) : $c GBP44.99
020 $a 1439840954 (hbk.)
020 $a 9781439840962 (ebk.)
020 $a 1439840962 (ebk.)
035 $a LIB-BW-103-012
040 $a DLC $b eng
050 0 0 $a QA279.5 $b .G45 2014
082 0 0 $a 519.5/42 $2 23
100 1 $a Gelman, Andrew. $3 686896
245 1 0 $a Bayesian data analysis / $c Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin.
250 $a 3rd ed.
260 # $a Boca Raton : $b CRC Press, $c 2014.
300 $a xiv, 661 p. : $b ill. ; $c 27 cm.
490 1 0 $a Chapman & Hall/CRC texts in statistical science.
504 $a Includes bibliographical references (pages 607-639) and indexes.
520 # $a "Preface This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics. The mathematics used in our book is basic probability and statistics, elementary calculus, and linear algebra. A review of probability notation is given in Chapter 1 along with a more detailed list of topics assumed to have been studied. The practical orientation of the book means that the reader's previous experience in probability, statistics, and linear algebra should ideally have included strong computational components. To write an introductory text alone would leave many readers with only a taste of the conceptual elements but no guidance for venturing into genuine practical applications, beyond those where Bayesian methods agree essentially with standard non-Bayesian analyses. On the other hand, we feel it would be a mistake to present the advanced methods without first introducing the basic concepts from our data-analytic perspective. Furthermore, due to the nature of applied statistics, a text on current Bayesian methodology would be incomplete without a variety of worked examples drawn from real applications. To avoid cluttering the main narrative, there are bibliographic notes at the end of each chapter and references at the end of the book"-- $c Provided by publisher.
650 # 0 $a Bayesian statistical decision theory. $3 551404