Handbook of Probabilistic Models

Herausgegeben:Samui, Pijush; Tien Bui, Dieu; Chakraborty, Subrata; Deo, Ravinesh

• Broschiertes Buch

Produktbeschreibung

Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences.

Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood,Bayesian inference, Bayesian update, kriging, copula-statistical models, and more.

Produktdetails

• Verlag: Butterworth-Heinemann / Elsevier Science & Technology
• Artikelnr. des Verlages: C2017-0-04723-7
• Englisch
• Abmessung: 27mm x 152mm x 229mm
• Gewicht: 880g
• ISBN-13: 9780128165140
• Artikelnr.: 56740194

Autorenporträt

Dr. Samui is an Associate Professor in the Department of Civil Engineering at NIT Patna, India. He received his PhD in Geotechnical Engineering from the Indian Institute of Science Bangalore, India, in 2008. His research interests include geohazard, earthquake engineering, concrete technology, pile foundation and slope stability, and application of AI for solving different problems in civil engineering. Dr. Samui is a repeat Elsevier editor but also a prolific contributor to journal papers, book chapters, and peer-reviewed conference proceedings.

Dieu Tien Bui is Professor in GIS, in the Department of Business and IT at the University of South-Eastern Norway, Norway. He obtained a Master of Engineering, at Hanoi University of Mining and Geology, Hanoi, Vietnam, a PhD at the Department of Mathematical Sciences and Technology (IMT), Norwegian University, and was postdoctoral researcher in the same department. His research interests include GIS, remote sensing, artificial intelligence and machine learning. He published journal and review articles, and book chapters. .

Inhaltsangabe

1. Monte Carlo Simulation
2. Stochastic Optimization Method
3. Reliability Analysis
4. Stochastic Finite Element Method
5. Kalman Filter
6. Random matrix
7. Markov Chain
8. Gaussian Process Regression
9. Logistic regression
10. Geostatistics
11. Kriging
12. Bayesian inference
13. Bayesian updating
14. Probabilistic Neural Network
15. SVM, Relevance vector machine