Handbook of Probabilistic Models

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.

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