Dynamic Programming and Bayesian Inference, Concepts and Applications

InTech, 2014. — 160 p. — ISBN: 9789535113645 The purpose of this book is to provide some applications of Bayesian optimization and dynamic programming. Dynamic programming and Bayesian inference have been both intensively and extensively developed during recent years. Because of these developments, interest in dynamic programming and Bayesian inference and their applications has greatly increased at all mathematical levels. Contents: Preface. Bayesian Networks for Supporting Model Based Predictive Control of Smart Buildings. Integration of Remotely Sensed Images and Electromagnetic Models into a Bayesian Approach for Soil Moisture Content Retrieval: Methodology and Effect of Prior Information. Optimizing Basel III Liquidity Coverage Ratios. Risk-Constrained Forward Trading Optimization by Stochastic Approximate Dynamic Programming. Using Dynamic Programming Based on Bayesian Inference in Selection Problems. with TOC BookMarkLinks. Dynamic programming and Bayesian inference have been both intensively and extensively developed during recent years. Because of these developments, interest in dynamic programming and Bayesian inference and their applications has greatly increased at all mathematical levels. The purpose of this book is to provide some applications of Bayesian optimization and dynamic programming