Preface

Dynamic programming and Bayesian inference have been both intensively and extensively developed during recent years. Because of these developments, interest in dynamic pro‐ gramming and Bayesian inference and their applications has greatly increased at all mathe‐ matical levels. The purpose of this book is to provide some applications of Bayesian optimization and dynamic programming.

The book focuses on a clear presentation of the main concepts and results, different models of optimization, with particular emphasis on dynamic programming and Bayesian models. It provides a description of basic material on dynamic programming and Bayesian infer‐ ence, as well as more recent developments in optimization models.

Throughout the book, the chapters concentrate on concepts, rather than mathematical detail, but every effort has been made to present the key theoretical results in as precise and rigor‐ ous a manner as possible, consistent with the overall level of the book.

Prerequisites for the book are some familiarity with concept of dynamic programming, and some knowledge of Bayesian models.

> **Mohammad Saber Fallah Nezhad** Associate Professor of Industrial Engineering,

Yazd University, Iran

**Section 1**

**Applications of Bayesian Inference**

**Applications of Bayesian Inference**

**Chapter 1**

**Bayesian Networks for Supporting Model Based**

Optimal behaviour is one the most desired features of contemporary technological systems. Challenges like secure operation, energy efficiency, and reliable performance call for the optimised behaviour of any systems that operate and interact in our living environment. The challenge in achieving optimised performances resides in the uncertainty that qualifies the environment surrounding technical systems. Whatever model drives the systems' behaviour, it must be able to face unforeseen events, to manage the vagueness of the sensing apparatus and the errors of the control devices. Bayesian statistics is one of the theoretical backgrounds that support the construction of systems which are able to act effectively inside complex environments. Bayesian statistics is grounded on the fundamental premise that all uncertain‐ ties should be represented and measured by probabilities. Then, the laws of probabilities apply to produce probabilistic inferences about any quantity, or collection of quantities, of interest. Bayesian inference can provide predictions about probability values pertaining time series or can model parameters in terms of probability distributions that represent and summarize current uncertain knowledge and beliefs. Bayesian inference uses a kind of direct causal or model-based knowledge to provide the crucial robustness needed to make the optimised behaviour of technical systems feasible in the real world [1]. Once this kind of models have been built, then theoretically sound evidence propagation algorithms are used to update the belief set about the external environment and about the system performance, on the basis of acquired evidence. This is the fundamental mechanism that drives the construction and the operation of intelligent systems based on Bayesian inference. This chapter describes a sample engineering application of this approach on a large scale. It concerns the design and the development of an intelligent building energy management system (smart BEMS) that is able

> © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Predictive Control of Smart Buildings**

Alessandro Carbonari, Massimo Vaccarini and

Additional information is available at the end of the chapter

Alberto Giretti

**1. Introduction**

http://dx.doi.org/10.5772/58470
