Pijush Samui, Dieu Tien Bui, Subrata Chakraborty, Ravinesh Deo
Handbook of Probabilistic Models (PDF) carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook; researchers; practitioners; and scientists will find detailed applications of the proposed methods; explanations of technical concepts; and the respective scientific approaches needed to solve the problem. This ebook provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields; ranging from conventional fields of civil engineering and mechanical engineering electrical; earth sciences; to electronics; agriculture; climate; mathematical sciences; water resource; and computer sciences.
Specific topics covered include minimax probability machine regression; relevance vector machine; stochastic finite element method; Monte Carlo simulations; random matrix; logistic regression; Kalman filter; stochastic optimization; maximum likelihood; Gaussian process regression; Bayesian update; Bayesian inference; copula-statistical models; kriging; and more.
- Applies probabilistic modeling to emerging areas in engineering
- Explains the application of advanced probabilistic models encompassing multidisciplinary research
- Provides an interdisciplinary approach to probabilistic models and their applications; thus solving a wide range of practical problems
NOTE: This only includes the ebook Handbook of Probabilistic Models in PDF.