Handbook of Probabilistic Models 1st Edition – Ebook Instant Download/Delivery ISBN(s): 9780128165140,0128165146,9780128165461, 0128165464
Product details:
- ISBN-10: 0128165464
- ISBN-13: 9780128165461
- Author: Dieu Tien Bui, Pijush Samui, Ravinesh Deo, Subrata Chakraborty
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.
Table contents:
Chapter 1. Fundamentals of reliability analysis
Chapter 2. Modeling wheat yield with data-intelligent algorithms: artificial neural network versus genetic programming and minimax probability machine regression
Chapter 3. Monthly rainfall forecasting with Markov Chain Monte Carlo simulations integrated with statistical bivariate copulas
Chapter 4. A model for quantitative fire risk assessment integrating agent-based model with automatic event tree analysis
Chapter 5. Prediction capability of polynomial neural network for uncertain buckling behavior of sandwich plates
Chapter 6. Development of copula-statistical drought prediction model using the Standardized Precipitation-Evapotranspiration Index
Chapter 7. An efficient approximation-based robust design optimization framework for large-scale structural systems
Chapter 8. Probabilistic seasonal rainfall forecasts using semiparametric d-vine copula-based quantile regression
Chapter 9. Geostatistics: principles and methods
Chapter 10. Adaptive H∞ Kalman filtering for stochastic systems with nonlinear uncertainties
Chapter 11. R for lifetime data modeling via probability distributions
Chapter 12. Probability-based approach for evaluating groundwater risk assessment in Sina basin, India
Chapter 13. Novel concepts for reliability analysis of dynamic structural systems
Chapter 14. Probabilistic neural networks: a brief overview of theory, implementation, and application
Chapter 15. Design of experiments for uncertainty quantification based on polynomial chaos expansion metamodels
Chapter 16. Stochastic response of primary–secondary coupled systems under uncertain ground excitation using generalized polynomial chaos method
Chapter 17. Stochastic optimization: stochastic diffusion search algorithm
Chapter 18. Resampling methods combined with Rao-Blackwellized Monte Carlo Data Association algorithm
Chapter 19. Back-propagation neural network modeling on the load–settlement response of single piles
Chapter 20. A Monte Carlo approach applied to sensitivity analysis of criteria impacts on solar PV site selection
Chapter 21. Stochastic analysis basics and application of statistical linearization technique on a controlled system with nonlinear viscous dampers
Chapter 22. A comparative assessment of ANN and PNN model for low-frequency stochastic free vibration analysis of composite plates
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