E-Book, Englisch, 254 Seiten, Web PDF
Paz / Rheinboldt Introduction to Probabilistic Automata
1. Auflage 2014
ISBN: 978-1-4832-6857-6
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, 254 Seiten, Web PDF
ISBN: 978-1-4832-6857-6
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark
Introduction to Probabilistic Automata deals with stochastic sequential machines, Markov chains, events, languages, acceptors, and applications. The book describes mathematical models of stochastic sequential machines (SSMs), stochastic input-output relations, and their representation by SSMs. The text also investigates decision problems and minimization-of-states problems arising from concepts of equivalence and coverings for SSMs. The book presents the theory of nonhomogeneous Markov chains and systems in mathematical terms, particularly in relation to asymptotic behavior, composition (direct sum or product), and decomposition. 'Word functions,' induced by Markov chains and valued Markov systems, involve characterization, equivalence, and representability by an underlying Markov chain or system. The text also discusses the closure properties of probabilistic languages, events and their relation to regular events, particularly with reference to definite, quasidefinite, and exclusive events. Probabilistic automata theory has applications in information theory, control, learning theory, pattern recognition, and time sharing in computer programming. Programmers, computer engineers, computer instructors, and students of computer science will find the collection highly valuable.
Autoren/Hrsg.
Weitere Infos & Material
1;Front Cover;1
2;Introduction to Probabilistic Automata;4
3;Copyright Page;5
4;Table of Contents;8
5;Dedication;6
6;Preface;10
7;Acknowledgments;12
8;Abbreviations;14
9;Notation;16
10;Preliminaries;20
11;Chapter I. Stochastic Sequential Machines;28
11.1;INTRODUCTION;28
11.2;A. THE MODEL;28
11.3;B. STATE THEORY AND EQUIVALENCE;46
11.4;C. INPUT-OUTPUT RELATIONS;82
12;Chapter II. Markov Chains;94
12.1;INTRODUCTION;94
12.2;A. NONHOMOGENEOUS MARKOV CHAINS AND SYSTEMS;94
12.3;B. OPERATION ON MARKOV SYSTEMS;128
12.4;C. WORD-FUNCTIONS;143
13;Chapter III. Events, Languages, and Acceptors;172
13.1;INTRODUCTION;172
13.2;A. EVENTS;172
13.3;B. CUT-POINT EVENTS;180
13.4;c. Quasidefinite PCEs;202
14;Chapter IV. Applications and Generalizations;222
14.1;INTRODUCTION;222
14.2;A. INFORMATION THEORY;222
14.3;B. RELIABILITY;223
14.4;C. LEARNING THEORY AND PATTERN RECOGNITION;223
14.5;D. CONTROL;224
14.6;.. OTHER APPLICATONS;224
14.7;F. EXTENSIONS AND CONNECTIONS TO OTHER THEORIES;224
15;References;226
16;Answers and Hints to Selected Exercises;236
17;Author Index;250
18;Subject Index;253
