Bayesian Machine Learning in Geotechnical Site Characterization is popular PDF and ePub book, written by Jianye Ching in 2024-08-07, it is a fantastic choice for those who relish reading online the Computers genre. Let's immerse ourselves in this engaging Computers book by exploring the summary and details provided below. Remember, Bayesian Machine Learning in Geotechnical Site Characterization can be Read Online from any device for your convenience.
Bayesian Machine Learning in Geotechnical Site Characterization Book PDF Summary
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.
Detail Book of Bayesian Machine Learning in Geotechnical Site Characterization PDF
- Author : Jianye Ching
- Release : 07 August 2024
- Publisher : CRC Press
- ISBN : 9781040097779
- Genre : Computers
- Total Page : 189 pages
- Language : English
- PDF File Size : 18,8 Mb
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