eBook - ePub

Bayesian Machine Learning in Geotechnical Site Characterization

Name: Bayesian Machine Learning in Geotechnical Site Characterization
ISBN: 9781040097823

Jianye Ching,

188 pages
English
ePUB (mobile friendly)
Available on iOS & Android

eBook - ePub

Bayesian Machine Learning in Geotechnical Site Characterization

Jianye Ching,

About this book

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.

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Yes, you can access Bayesian Machine Learning in Geotechnical Site Characterization by Jianye Ching in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Civil Engineering. We have over one million books available in our catalogue for you to explore.