MARC details
| 000 -LEADER |
|---|
| fixed length control field |
01825nam a2200253 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
ISI Library, Kolkata |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20260512105750.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
220920s2022 xxu b 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781639870707 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
1639870709 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
ISI Library |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Edition number |
23 |
| Classification number |
SA.161 |
| Item number |
G778 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Gray, Arthur |
| Relator term |
author |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Bayesian inference : |
| Remainder of title |
statistical and probabilistic Mathematics / |
| Statement of responsibility, etc |
Arthur Gray. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
2022 edition. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
N.Y. : |
| Name of publisher, distributor, etc |
Murphy & Moore, |
| Date of publication, distribution, etc |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
239 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
28 cm |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
Bayesian Inference: Statistical and Probabilistic Mathematics by Arthur Gray provides a structured introduction to Bayesian inference as a coherent framework for reasoning under uncertainty, contrasting it with frequentist approaches and emphasizing probability as a measure of belief. The book develops the core idea of using Bayes’ theorem to update prior beliefs with new evidence through prior distributions, likelihood functions, and posterior distributions, while also discussing both subjective and objective choices of priors. It further explores practical modeling techniques such as conjugate priors, hierarchical models, and predictive distributions, alongside computational methods like Markov Chain Monte Carlo (MCMC) for handling complex problems. Through applications in areas such as machine learning, decision theory, and scientific data analysis, the text highlights probabilistic interpretation over traditional point estimates, ultimately bridging theoretical foundations with practical implementation of Bayesian methods. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Bayesian statistical decision theory. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Mathematical statistics. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Probabilities. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |
