MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02304nam a22003017a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
ZW-GwMSU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20200608121750.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
200311b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781108703741 |
| 040 ## - CATALOGING SOURCE |
|---|
| Language of cataloging |
English |
| Transcribing agency |
MSU |
| Description conventions |
rda |
| 041 ## - |
|---|
| -- |
eng |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA279.5 TUR |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Turkman, M. Antonia Amaral |
| Relator term |
author |
| -- |
University of Lisbon |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Computational bayesian statistics |
| Remainder of title |
an introduction |
| Statement of responsibility, etc |
M Antonia Amaral Turkman, Carlos Daniel Paulino and Peter Muller |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Cambridge |
| Name of publisher, distributor, etc |
Cambridge University Press |
| Date of publication, distribution, etc |
2019 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xi, 243 pages |
| Dimensions |
22 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| source |
rdamedia |
| media type term |
unmediated |
| media type code |
n |
| 338 ## - CARRIER TYPE |
|---|
| source |
rdacarrier |
| carrier type term |
volume |
| carrier type code |
nc |
| 490 ## - SERIES STATEMENT |
|---|
| Series statement |
Textbooks with ISBA |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes an index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
1. Bayesian inference; 2. Representation of prior information; 3. Bayesian inference in basic problems; 4. Inference by Monte Carlo methods; 5. Model assessment; 6. Markov chain Monte Carlo methods; 7. Model selection and transdimensional MCMC; 8. Methods based on analytic approximations; 9. Software |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Bayesian statistical decision theory |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Paulino, Carlos Daniel |
| Relator term |
author |
| -- |
University of Lisbon |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Muller, Peter |
| Relator term |
author |
| -- |
University of Texas, Austin |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Book |