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Theory of Information and its Value

  • Ruslan L. Stratonovich
  • Roman V. Belavkin
  • Panos M. Pardalos
  • Jose C. Principe
Book

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Roman V. Belavkin, Panos M. Pardalos, Jose C. Principe
    Pages 1-33
  3. Roman V. Belavkin, Panos M. Pardalos, Jose C. Principe
    Pages 35-51
  4. Roman V. Belavkin, Panos M. Pardalos, Jose C. Principe
    Pages 53-75
  5. Roman V. Belavkin, Panos M. Pardalos, Jose C. Principe
    Pages 77-101
  6. Roman V. Belavkin, Panos M. Pardalos, Jose C. Principe
    Pages 103-171
  7. Roman V. Belavkin, Panos M. Pardalos, Jose C. Principe
    Pages 173-215
  8. Roman V. Belavkin, Panos M. Pardalos, Jose C. Principe
    Pages 217-247
  9. Roman V. Belavkin, Panos M. Pardalos, Jose C. Principe
    Pages 249-288
  10. Roman V. Belavkin, Panos M. Pardalos, Jose C. Principe
    Pages 289-325
  11. Roman V. Belavkin, Panos M. Pardalos, Jose C. Principe
    Pages 327-352
  12. Roman V. Belavkin, Panos M. Pardalos, Jose C. Principe
    Pages 353-390
  13. Roman V. Belavkin, Panos M. Pardalos, Jose C. Principe
    Pages 391-408
  14. Back Matter
    Pages 409-419

About this book

Introduction

This English version of Ruslan L. Stratonovich’s Theory of Information (1975) builds on theory and provides methods, techniques, and concepts toward utilizing critical applications. Unifying theories of information, optimization, and statistical physics, the value of information theory has gained recognition in data science, machine learning, and artificial intelligence. With the emergence of a data-driven economy, progress in machine learning, artificial intelligence algorithms, and increased computational resources, the need for comprehending information is essential. This book is even more relevant today than when it was first published in 1975. It extends the classic work of R.L. Stratonovich, one of the original developers of the symmetrized version of stochastic calculus and filtering theory, to name just two topics.

Each chapter begins with basic, fundamental ideas, supported by clear examples; the material then advances to great detail and depth.  The reader is not required to be familiar with the more difficult and specific material. Rather, the treasure trove of examples of stochastic processes and problems makes this book accessible to a wide readership of researchers, postgraduates, and undergraduate students in mathematics, engineering, physics and computer science who are specializing in information theory, data analysis, or machine learning.

Keywords

Rouslan Leontievich Stratonovich Theory of Random Noise Conditional Markov Processes adaptive Bayesian inference information theory statistical thermodynamics Value of Information quantum information theory filtering theory non-commutative probability Boltzmann distribution exponential family distributions machine learning cognitive modelling temperature parameter AI algorithms data-driven economy stochastic processes data analysis develop stochastic calculus

Authors and affiliations

  • Ruslan L. Stratonovich
    • 1
  1. 1.(deceased)MoscowRussia

Editors and affiliations

  • Roman V. Belavkin
    • 1
  • Panos M. Pardalos
    • 2
  • Jose C. Principe
    • 3
  1. 1.Faculty of Science and TechnologyMiddlesex UniversityLondonUK
  2. 2.Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA
  3. 3.Electrical & Computer EngineeringUniversity of FloridaGainesvilleUSA

Bibliographic information