|
|
|
|
| LEADER |
00000nam a22000005i 4500 |
| 001 |
b3331508 |
| 003 |
MWH |
| 005 |
20191029052521.0 |
| 007 |
cr nn 008mamaa |
| 008 |
100301s2006 xxu| s |||| 0|eng d |
| 020 |
|
|
|a 9780387354330
|
| 024 |
7 |
|
|a 10.1007/978-0-387-35433-0
|2 doi
|
| 035 |
|
|
|a (DE-He213)978-0-387-35433-0
|
| 050 |
|
4 |
|a E-Book
|
| 072 |
|
7 |
|a PBT
|2 bicssc
|
| 072 |
|
7 |
|a MAT029000
|2 bisacsh
|
| 072 |
|
7 |
|a PBT
|2 thema
|
| 100 |
1 |
|
|a Ghosh, Jayanta K.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
|
| 245 |
1 |
3 |
|a An Introduction to Bayesian Analysis
|h [electronic resource] :
|b Theory and Methods /
|c by Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta.
|
| 250 |
|
|
|a 1st ed. 2006.
|
| 264 |
|
1 |
|a New York, NY :
|b Springer New York :
|b Imprint: Springer,
|c 2006.
|
| 300 |
|
|
|a XIII, 354 p.
|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 Texts in Statistics,
|x 1431-875X
|
| 490 |
1 |
|
|a Springer eBook Collection
|
| 505 |
0 |
|
|a Statistical Preliminaries -- Bayesian Inference and Decision Theory -- Utility, Prior, and Bayesian Robustness -- Large Sample Methods -- Choice of Priors for Low-dimensional Parameters -- Hypothesis Testing and Model Selection -- Bayesian Computations -- Some Common Problems in Inference -- High-dimensional Problems -- Some Applications.
|
| 520 |
|
|
|a This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical computing. Advances in both low-dimensional and high-dimensional problems are covered, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques. Many topics are at the cutting edge of statistical research. Solutions to common inference problems appear throughout the text along with discussion of what prior to choose. There is a discussion of elicitation of a subjective prior as well as the motivation, applicability, and limitations of objective priors. By way of important applications the book presents microarrays, nonparametric regression via wavelets as well as DMA mixtures of normals, and spatial analysis with illustrations using simulated and real data. Theoretical topics at the cutting edge include high-dimensional model selection and Intrinsic Bayes Factors, which the authors have successfully applied to geological mapping. The style is informal but clear. Asymptotics is used to supplement simulation or understand some aspects of the posterior. J.K. Ghosh has been Director and Jawaharlal Nehru Professor at the Indian Statistical Institute and President of the International Statistical Institute. He is currently a professor of statistics at Purdue University and professor emeritus at the Indian Statistical Institute. He has been the editor of Sankhya and has served on the editorial boards of several journals including the Annals of Statistics. His current interests in Bayesian analysis include asymptotics, nonparametric methods, high-dimensional model selection, reliability and survival analysis, bioinformatics, astrostatistics and sparse and not so sparse mixtures. Mohan Delampady and Tapas Samanta are both professors of statistics at the Indian Statistical Institute and both are interested in Bayesian inference, specifically in topics such as model selection, asymptotics, robustness and nonparametrics.
|
| 590 |
|
|
|a Loaded electronically.
|
| 590 |
|
|
|a Electronic access restricted to members of the Holy Cross Community.
|
| 650 |
|
0 |
|a Statistics .
|
| 690 |
|
|
|a Electronic resources (E-books)
|
| 700 |
1 |
|
|a Delampady, Mohan.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
|
| 700 |
1 |
|
|a Samanta, Tapas.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
|
| 710 |
2 |
|
|a SpringerLink (Online service)
|
| 773 |
0 |
|
|t Springer eBooks
|
| 830 |
|
0 |
|a Springer Texts in Statistics,
|x 1431-875X
|
| 830 |
|
0 |
|a Springer eBook Collection.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://doi.org/10.1007/978-0-387-35433-0
|3 Click to view e-book
|t 0
|
| 907 |
|
|
|a .b33315085
|b 04-18-22
|c 02-26-20
|
| 998 |
|
|
|a he
|b 02-26-20
|c m
|d @
|e -
|f eng
|g xxu
|h 3
|i 1
|
| 912 |
|
|
|a ZDB-2-SMA
|
| 950 |
|
|
|a Mathematics and Statistics (Springer-11649)
|
| 902 |
|
|
|a springer purchased ebooks
|
| 903 |
|
|
|a SEB-COLL
|
| 945 |
|
|
|f - -
|g 1
|h 0
|j - -
|k - -
|l he
|o -
|p $0.00
|q -
|r -
|s b
|t 38
|u 0
|v 0
|w 0
|x 0
|y .i22446709
|z 02-26-20
|
| 999 |
f |
f |
|i 06c58621-028a-50dc-85ab-4f8c78bacc2b
|s b96654c7-29dc-53f5-a68a-7d1904604627
|t 0
|
| 952 |
f |
f |
|p Online
|a College of the Holy Cross
|b Main Campus
|c E-Resources
|d Online
|t 0
|e E-Book
|h Library of Congress classification
|i Elec File
|