Bayesian Inference for Probabilistic Risk Assessment (Record no. 106455)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 03710nam a22004815i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-1-84996-187-5 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20140220083736.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 110829s2011 xxk| s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781849961875 |
| -- | 978-1-84996-187-5 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-1-84996-187-5 |
| Source of number or code | doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | TA169.7 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | T55-T55.3 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | TA403.6 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | TGPR |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | TEC032000 |
| Source | bisacsh |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 658.56 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kelly, Dana. |
| Relator term | author. |
| 245 10 - TITLE STATEMENT | |
| Title | Bayesian Inference for Probabilistic Risk Assessment |
| Medium | [electronic resource] : |
| Remainder of title | A Practitioner's Guidebook / |
| Statement of responsibility, etc | by Dana Kelly, Curtis Smith. |
| 264 #1 - | |
| -- | London : |
| -- | Springer London, |
| -- | 2011. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | XII, 228 p. |
| Other physical details | online resource. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 490 1# - SERIES STATEMENT | |
| Series statement | Springer Series in Reliability Engineering, |
| International Standard Serial Number | 1614-7839 |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 1. Introduction and Motivation -- 2. Introduction to Bayesian Inference -- 3. Bayesian Inference for Common Aleatory Models -- 4. Bayesian Model Checking -- 5. Time Trends for Binomial and Poisson Data -- 6. Checking Convergence to Posterior Distribution -- 7. Hierarchical Bayes Models for Variability -- 8. More Complex Models for Random Durations -- 9. Modeling Failure with Repair -- 10. Bayesian Treatment of Uncertain Data -- 11. Bayesian Regression Models -- 12. Bayesian Inference for Multilevel Fault Tree Models -- 13. Additional Topics. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing inference on these problems. Inference in the book employs a modern computational approach known as Markov chain Monte Carlo (MCMC). The MCMC approach may be implemented using custom-written routines or existing general purpose commercial or open-source software. This book uses an open-source program called OpenBUGS (commonly referred to as WinBUGS) to solve the inference problems that are described. A powerful feature of OpenBUGS is its automatic selection of an appropriate MCMC sampling scheme for a given problem. The authors provide analysis “building blocks” that can be modified, combined, or used as-is to solve a variety of challenging problems. The MCMC approach used is implemented via textual scripts similar to a macro-type programming language. Accompanying most scripts is a graphical Bayesian network illustrating the elements of the script and the overall inference problem being solved. Bayesian Inference for Probabilistic Risk Assessment also covers the important topics of MCMC convergence and Bayesian model checking. Bayesian Inference for Probabilistic Risk Assessment is aimed at scientists and engineers who perform or review risk analyses. It provides an analytical structure for combining data and information from various sources to generate estimates of the parameters of uncertainty distributions used in risk and reliability models. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | System safety. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Quality Control, Reliability, Safety and Risk. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Smith, Curtis. |
| Relator term | author. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY | |
| Title | Springer eBooks |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9781849961868 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | Springer Series in Reliability Engineering, |
| -- | 1614-7839 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-1-84996-187-5 |
| 912 ## - | |
| -- | ZDB-2-ENG |
No items available.