國立高雄大學圖資館 |

Language: English

Back

Bayesian nonparametric methods for missing data and causal inference /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bayesian nonparametric methods for missing data and causal inference /Michael J. Daniels, Antonio Linero, and Jason Roy.
Author:	Daniels, M. J.
other author:	Linero, Antonio,
Description:	xiv, 248pages :illustrations ;25cm.
Subject:	Nonparametric statistics.
ISBN:	9780429324222$q(ebook)

Bayesian nonparametric methods for missing data and causal inference /
Daniels, M. J.

Bayesian nonparametric methods for missing data and causal inference /Michael J. Daniels, Antonio Linero, and Jason Roy. - First edition. - xiv, 248pages :illustrations ;25cm. - Chapman & Hall/CRC monographs on statistics and applied probability.

Includes bibliographical references and index.

"Bayesian nonparametric (BNP) methods can be used to flexibly model joint or conditional distributions, as well as functional relationships. These methods, along with causal and/or missingness assumptions, can be used with the g-formula to infer causal effects"--

ISBN: 9780429324222$q(ebook)

LCCN: 2023007544Subjects--Topical Terms:

182700
Nonparametric statistics.

LC Class. No.: QA278.8

Dewey Class. No.: 519.5/4

Bayesian nonparametric methods for missing data and causal inference /
LDR:01776cam 22003858i 4500 001 655140
003 OCoLC
005 20240606103729.0
006 m o d
007 cr |||||||||||
008 240606s2024 flu ob 001 0 eng
010 $a 2023007544
020 $a 9780429324222$q(ebook)
020 $a 0429324227
020 $z 9780367341008$q(hardback)
020 $z 9781032456942$q(paperback)
035 $a (OCoLC)1404058481
035 $a on1404058481
040 $a DLC $b eng $e rda $c DLC $d OCLCO $d OCLCF $d OCLCO
042 $a pcc
049 $a NUKM
050 0 0 $a QA278.8
082 0 0 $a 519.5/4 $2 23/eng/20231016
100 1 $a Daniels, M. J. $3 490500
245 1 0 $a Bayesian nonparametric methods for missing data and causal inference / $c Michael J. Daniels, Antonio Linero, and Jason Roy.
250 $a First edition.
264 1 $a Boca Raton : $b CRC Press, $c 2024.
300 $a xiv, 248pages : $b illustrations ; $c 25cm.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
490 0 $a Chapman & Hall/CRC monographs on statistics and applied probability
504 $a Includes bibliographical references and index.
520 $a "Bayesian nonparametric (BNP) methods can be used to flexibly model joint or conditional distributions, as well as functional relationships. These methods, along with causal and/or missingness assumptions, can be used with the g-formula to infer causal effects"-- $c Provided by publisher.
588 $a Description based on print version record and CIP data provided by publisher; resource not viewed.
650 0 $a Nonparametric statistics. $3 182700
650 0 $a Bayesian statistical decision theory. $3 182005
650 0 $a Missing observations (Statistics) $3 182525
650 6 $a Statistique non paramétrique. $3 966384
650 6 $a Théorie de la décision bayésienne. $3 966385
650 6 $a Observations manquantes (Statistique) $3 265582
650 7 $a Bayesian statistical decision theory $v Textbooks. $3 443102
650 7 $a Nonparametric statistics $v Textbooks. $3 445332
700 1 $a Linero, Antonio, $e author. $3 966304
700 1 $a Roy, Jason $q (Jason Allen), $e author. $3 966305
776 0 8 $i Print version: $a Daniels, M. J. $t Bayesian nonparametric methods for missing data and causal inference $b First edition. $d Boca Raton : CRC Press, 2024 $z 9780367341008 $w (DLC) 2023007543
994 $a C0 $b TWNUK