Torrent details for:

Textbook in PDF format Preface Objective and Audience Guide to the Chapters Acknowledgments Symbols and Notation Gaussian Process Preliminaries Introduction Fundamentals Gaussian Process Regression GP Likelihood and Hyperparameters Estimation and Likelihood Examples and Discussion Hyperparameter Sensitivity in GPR Universal Kriging and Varying Prior Means GPs as Kernel Smoothing and Kernel Ridge Regression Closing Notes Further Reading Covariance Kernels First Examples and Smoothness Classes and Properties of Kernels Stationary Kernels Nonstationary Kernels Kernel Composition and Engineering Model Selection Example: Kernel Fitness Convergence and Universal Approximation Connections with SDEs and Other Processes Further Reading Advanced GP Modeling Topics Heteroskedastic GPs Alternative Likelihood Functions Gaussian Process Regression with Student t-Noise Student-t Process Regression with Student-t Noise Gaussian Process GLM Multi-Output GPs Localization Inducing Points Variational Gaussian Processes Updating Equations for GPs Further Reading Option Pricing and Sensitivities Learning to Price Options Surrogate Ingredients Option Sensitivities GP Gradients Illustration: Estimating Greeks in the Black-Scholes Model Constrained GPs and No-Arbitrage Portfolio Modeling and Credit Valuation Adjustments Further Reading Optimal Stopping Regression Monte Carlo RMC GP Features Training Designs Illustration: Bermudan Options Active Learning and Adaptive Batching Further Reading Non-Parametric Modeling of Financial Structures Modeling Term Structure Kriging of Commodity Curves Modeling Implied Volatility Swaption Cubes Mortality Rate Surfaces Illustration: Danish Mortality Valuation of Variable Annuities Further Reading Stochastic Control Switching Control Continuous Control Impulse Control Further Reading Mathematical Background Matrices and Linear Algebra Multivariate Normal Distributions Differentiability, Smoothness, and Function Spaces Sobolev Spaces References Index