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.