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. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. Dedication - p. v 6. Contributors - p. xix 7. Chapter 1. Fundamentals of reliability analysis - p. 1 8. Chapter 2. Modeling wheat yield with data-intelligent algorithms: artificial neural network versus genetic programming and minimax probability machine regression - p. 37 9. Chapter 3. Monthly rainfall forecasting with Markov Chain Monte Carlo simulations integrated with statistical bivariate copulas - p. 89 10. Chapter 4. A model for quantitative fire risk assessment integrating agent-based model with automatic event tree analysis - p. 107 11. Chapter 5. Prediction capability of polynomial neural network for uncertain buckling behavior of sandwich plates - p. 131 12. Chapter 6. Development of copula-statistical drought prediction model using the Standardized Precipitation-Evapotranspiration Index - p. 141 13. Chapter 7. An efficient approximation-based robust design optimization framework for large-scale structural systems - p. 179 14. Chapter 8. Probabilistic seasonal rainfall forecasts using semiparametric d-vine copula-based quantile regression - p. 203 15. Chapter 9. Geostatistics: principles and methods - p. 229 16. Chapter 10. Adaptive H∞ Kalman filtering for stochastic systems with nonlinear uncertainties - p. 243 17. Chapter 11. R for lifetime data modeling via probability distributions - p. 265 18. Chapter 12. Probability-based approach for evaluating groundwater risk assessment in Sina basin, India - p. 289 19. Chapter 13. Novel concepts for reliability analysis of dynamic structural systems - p. 305 20. Chapter 14. Probabilistic neural networks: a brief overview of theory, implementation, and application - p. 347 21. Chapter 15. Design of experiments for uncertainty quantification based on polynomial chaos expansion metamodels - p. 369 22. Chapter 16. Stochastic response of primary–secondary coupled systems under uncertain ground excitation using generalized polynomial chaos method - p. 383 23. Chapter 17. Stochastic optimization: stochastic diffusion search algorithm - p. 437 24. Chapter 18. Resampling methods combined with Rao-Blackwellized Monte Carlo Data Association algorithm - p. 449 25. Chapter 19. Back-propagation neural network modeling on the load–settlement response of single piles - p. 467 26. Chapter 20. A Monte Carlo approach applied to sensitivity analysis of criteria impacts on solar PV site selection - p. 489 27. Chapter 21. Stochastic analysis basics and application of statistical linearization technique on a controlled system with nonlinear viscous dampers - p. 505 28. Chapter 22. A comparative assessment of ANN and PNN model for low-frequency stochastic free vibration analysis of composite plates - p. 527 29. Index - p. 549
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. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. Dedication - p. v 6. Contributors - p. xix 7. Chapter 1. Fundamentals of reliability analysis - p. 1 8. Chapter 2. Modeling wheat yield with data-intelligent algorithms: artificial neural network versus genetic programming and minimax probability machine regression - p. 37 9. Chapter 3. Monthly rainfall forecasting with Markov Chain Monte Carlo simulations integrated with statistical bivariate copulas - p. 89 10. Chapter 4. A model for quantitative fire risk assessment integrating agent-based model with automatic event tree analysis - p. 107 11. Chapter 5. Prediction capability of polynomial neural network for uncertain buckling behavior of sandwich plates - p. 131 12. Chapter 6. Development of copula-statistical drought prediction model using the Standardized Precipitation-Evapotranspiration Index - p. 141 13. Chapter 7. An efficient approximation-based robust design optimization framework for large-scale structural systems - p. 179 14. Chapter 8. Probabilistic seasonal rainfall forecasts using semiparametric d-vine copula-based quantile regression - p. 203 15. Chapter 9. Geostatistics: principles and methods - p. 229 16. Chapter 10. Adaptive H∞ Kalman filtering for stochastic systems with nonlinear uncertainties - p. 243 17. Chapter 11. R for lifetime data modeling via probability distributions - p. 265 18. Chapter 12. Probability-based approach for evaluating groundwater risk assessment in Sina basin, India - p. 289 19. Chapter 13. Novel concepts for reliability analysis of dynamic structural systems - p. 305 20. Chapter 14. Probabilistic neural networks: a brief overview of theory, implementation, and application - p. 347 21. Chapter 15. Design of experiments for uncertainty quantification based on polynomial chaos expansion metamodels - p. 369 22. Chapter 16. Stochastic response of primary–secondary coupled systems under uncertain ground excitation using generalized polynomial chaos method - p. 383 23. Chapter 17. Stochastic optimization: stochastic diffusion search algorithm - p. 437 24. Chapter 18. Resampling methods combined with Rao-Blackwellized Monte Carlo Data Association algorithm - p. 449 25. Chapter 19. Back-propagation neural network modeling on the load–settlement response of single piles - p. 467 26. Chapter 20. A Monte Carlo approach applied to sensitivity analysis of criteria impacts on solar PV site selection - p. 489 27. Chapter 21. Stochastic analysis basics and application of statistical linearization technique on a controlled system with nonlinear viscous dampers - p. 505 28. Chapter 22. A comparative assessment of ANN and PNN model for low-frequency stochastic free vibration analysis of composite plates - p. 527 29. Index - p. 549
