Handbook of probabilistic models 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.