Introduction to Probabilistic Automata
eBook - PDF

Introduction to Probabilistic Automata

  1. 254 pages
  2. English
  3. PDF
  4. Only available on web
eBook - PDF

Introduction to Probabilistic Automata

Book details
Table of contents
Citations

About This Book

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.

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Yes, you can access Introduction to Probabilistic Automata by Azaria Paz, Werner Rheinboldt in PDF and/or ePUB format, as well as other popular books in Mathematics & Mathematics General. We have over one million books available in our catalogue for you to explore.

Information

Year
2014
ISBN
9781483268576

Table of contents

  1. Front Cover
  2. Introduction to Probabilistic Automata
  3. Copyright Page
  4. Table of Contents
  5. Dedication
  6. Preface
  7. Acknowledgments
  8. Abbreviations
  9. Notation
  10. Preliminaries
  11. Chapter I. Stochastic Sequential Machines
  12. Chapter II. Markov Chains
  13. Chapter III. Events, Languages, and Acceptors
  14. Chapter IV. Applications and Generalizations
  15. References
  16. Answers and Hints to Selected Exercises
  17. Author Index
  18. Subject Index