1st ed. 2020 Edition
by Ruslan L. Stratonovich (Author), Roman V. Belavkin (Editor), Panos M. Pardalos (Editor), Jose C. Principe (Editor)
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.
