Bayesian Machine Learning in Geotechnical Site Characterization /
Ching, Jianye,
Bayesian Machine Learning in Geotechnical Site Characterization / Jianye Ching. - First edition. - 1 online resource (xii, 176 pages). - Challenges in Geotechnical and Rock Engineering .
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.
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.
9781003309765 1003309763 9781040097779 1040097774 9781040097823 1040097820
10.1201/9781003309765 doi
--Data processing.
TA705
Bayesian Machine Learning in Geotechnical Site Characterization / Jianye Ching. - First edition. - 1 online resource (xii, 176 pages). - Challenges in Geotechnical and Rock Engineering .
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.
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.
9781003309765 1003309763 9781040097779 1040097774 9781040097823 1040097820
10.1201/9781003309765 doi
--Data processing.
TA705