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.