Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Bayesian Nonparametric Data Analysis
~
Müller, Peter.
Bayesian Nonparametric Data Analysis
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Bayesian Nonparametric Data Analysis/ by Peter Müller, Fernando Andres Quintana, Alejandro Jara, Tim Hanson.
Author:
Müller, Peter.
other author:
Quintana, Fernando Andres.
Description:
XIV, 193 p. 59 illus., 10 illus. in color.online resource. :
Contained By:
Springer Nature eBook
Subject:
Statistics . -
Online resource:
https://doi.org/10.1007/978-3-319-18968-0
ISBN:
9783319189680
Bayesian Nonparametric Data Analysis
Müller, Peter.
Bayesian Nonparametric Data Analysis
[electronic resource] /by Peter Müller, Fernando Andres Quintana, Alejandro Jara, Tim Hanson. - 1st ed. 2015. - XIV, 193 p. 59 illus., 10 illus. in color.online resource. - Springer Series in Statistics,0172-7397. - Springer Series in Statistics,.
Preface -- Acronyms -- 1.Introduction -- 2.Density Estimation - DP Models -- 3.Density Estimation - Models Beyond the DP -- 4.Regression -- 5.Categorical Data -- 6.Survival Analysis -- 7.Hierarchical Models -- 8.Clustering and Feature Allocation -- 9.Other Inference Problems and Conclusions -- Appendix: DP package.
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
ISBN: 9783319189680
Standard No.: 10.1007/978-3-319-18968-0doiSubjects--Topical Terms:
1253516
Statistics .
LC Class. No.: QA276-280
Dewey Class. No.: 519.5
Bayesian Nonparametric Data Analysis
LDR
:02537nam a22004095i 4500
001
962366
003
DE-He213
005
20200630013011.0
007
cr nn 008mamaa
008
201211s2015 gw | s |||| 0|eng d
020
$a
9783319189680
$9
978-3-319-18968-0
024
7
$a
10.1007/978-3-319-18968-0
$2
doi
035
$a
978-3-319-18968-0
050
4
$a
QA276-280
072
7
$a
PBT
$2
bicssc
072
7
$a
MAT029000
$2
bisacsh
072
7
$a
PBT
$2
thema
082
0 4
$a
519.5
$2
23
100
1
$a
Müller, Peter.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1257225
245
1 0
$a
Bayesian Nonparametric Data Analysis
$h
[electronic resource] /
$c
by Peter Müller, Fernando Andres Quintana, Alejandro Jara, Tim Hanson.
250
$a
1st ed. 2015.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2015.
300
$a
XIV, 193 p. 59 illus., 10 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Springer Series in Statistics,
$x
0172-7397
505
0
$a
Preface -- Acronyms -- 1.Introduction -- 2.Density Estimation - DP Models -- 3.Density Estimation - Models Beyond the DP -- 4.Regression -- 5.Categorical Data -- 6.Survival Analysis -- 7.Hierarchical Models -- 8.Clustering and Feature Allocation -- 9.Other Inference Problems and Conclusions -- Appendix: DP package.
520
$a
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
650
0
$a
Statistics .
$3
1253516
650
1 4
$a
Statistical Theory and Methods.
$3
671396
650
2 4
$a
Statistics and Computing/Statistics Programs.
$3
669775
650
2 4
$a
Statistics for Life Sciences, Medicine, Health Sciences.
$3
670172
700
1
$a
Quintana, Fernando Andres.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1257226
700
1
$a
Jara, Alejandro.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1257227
700
1
$a
Hanson, Tim.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1257228
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783319189697
776
0 8
$i
Printed edition:
$z
9783319189673
776
0 8
$i
Printed edition:
$z
9783319368429
830
0
$a
Springer Series in Statistics,
$x
0172-7397
$3
1257229
856
4 0
$u
https://doi.org/10.1007/978-3-319-18968-0
912
$a
ZDB-2-SMA
912
$a
ZDB-2-SXMS
950
$a
Mathematics and Statistics (SpringerNature-11649)
950
$a
Mathematics and Statistics (R0) (SpringerNature-43713)
based on 0 review(s)
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login