Handbook of Probabilistic Models, (PDF) carefully explores the application of advanced probabilistic models in conventional engineering fields. In this complete handbook, researchers, practitioners, and scientists will explore detailed explanations of technical concepts, applications of the proposed methods, and the particular scientific approaches needed to solve the problem. This ebook provides an interdisciplinary approach that makes advanced probabilistic models for engineering fields, ranging from conventional fields of civil engineering and mechanical engineering to electronics, earth sciences, climate, electrical, agriculture, water resource, computer sciences, and mathematical sciences.
Particular topics covered include minimax probability machine regression, stochastic finite element method, logistic regression, Monte Carlo simulations, relevance vector machine, random matrix, Kalman filter, stochastic optimization, Gaussian process regression, maximum likelihood, Bayesian update, kriging, Bayesian inference, copula-statistical models, and more.
- Employs probabilistic modeling to emerging areas in engineering
- Explains the application of advanced probabilistic models including multidisciplinary research
- Offers an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems
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