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Bayesian Machine Learning in Geotechnical Site Characterization / (Record no. 67570)

MARC details
000 -LEADER
fixed length control field 03160cam a2200505 i 4500
001 - CONTROL NUMBER
control field 9781003309765
003 - CONTROL NUMBER IDENTIFIER
control field FlBoTFG
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251030122235.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m o d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr cnu|||unuuu
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240715s2024 flu go 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency OCoLC-P
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency OCoLC-P
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781003309765
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1003309763
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9781032314433
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9781032314419
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781040097779
Qualifying information (electronic bk. : PDF)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1040097774
Qualifying information (electronic bk. : PDF)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781040097823
Qualifying information (electronic bk. : EPUB)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1040097820
Qualifying information (electronic bk. : EPUB)
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1201/9781003309765
Source of number or code doi
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1446216174
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC-P)1446216174
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA705
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM
Subject category code subdivision 094000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code TN
Source bicssc
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Ching, Jianye,
Relator term author.
Affiliation National Taiwan University, Taipei.
-- https://id.oclc.org/worldcat/entity/E39PCjBwFfgtrhp9PWPGpKrwYd
9 (RLIN) 93551
245 10 - TITLE STATEMENT
Title Bayesian Machine Learning in Geotechnical Site Characterization /
Statement of responsibility, etc. Jianye Ching.
250 ## - EDITION STATEMENT
Edition statement First edition.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Boca Raton :
Name of producer, publisher, distributor, manufacturer CRC Press,
Date of production, publication, distribution, manufacture, or copyright notice 2024.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (xii, 176 pages).
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
490 0# - SERIES STATEMENT
Series statement Challenges in Geotechnical and Rock Engineering
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 1. Bayesian Approach.2. Review of Probability and Models.3. Bayesian Parameter Estimation and Prediction.4. Geotechnical Data and Bayesian Modeling.5. Full-scale Real Case Study.
520 ## - SUMMARY, ETC.
Summary, etc. Bayesian data analysis and modelling linked with machine learning offers a new tool for handling geotechnical data. This book presents recent advancements made by the author in the area of probabilistic geotechnical site characterization. Two types of correlation play central roles in geotechnical site characterization: cross-correlation among soil properties and spatial-correlation in the underground space. The book starts with the introduction of Bayesian notion of probability "degree of belief", showing that well-known probability axioms can be obtained by Boolean logic and the definition of plausibility function without the use of the notion "relative frequency". It then reviews probability theories and useful probability models for cross-correlation and spatial correlation. Methods for Bayesian parameter estimation and prediction are also presented, and the use of these methods demonstrated with geotechnical site characterization examples. Bayesian Machine Learning in Geotechnical Site Characterization suits consulting engineers and graduate students in the area.
588 ## - SOURCE OF DESCRIPTION NOTE
Source of description note OCLC-licensed vendor bibliographic record.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element
General subdivision Data processing.
9 (RLIN) 93552
856 40 - ELECTRONIC LOCATION AND ACCESS
Materials specified Taylor & Francis
Uniform Resource Identifier <a href="https://www.taylorfrancis.com/books/9781003309765">https://www.taylorfrancis.com/books/9781003309765</a>
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified OCLC metadata license agreement
Uniform Resource Identifier <a href="http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf">http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type

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