*Markov Theory of Stochastic Process Estimation* by Mikhail Semyonovich Yarlykov and Mikhail Arkadyevich Mironov is an engaging and in-depth exploration of probability theory and statistics. This book is a valuable resource for students, graduate researchers, educators, and professionals working in fields such as economics, engineering, biology, and information technology. At its core, the authors focus on Markov models, which serve as the foundation for analyzing and evaluating random processes. The book offers a balanced mix of theoretical insights and practical applications, making it especially useful for those eager to apply their knowledge to real-world problems. Unlike many other works on probability theory, this publication emphasizes the estimation of processes, providing deeper understanding of how to make predictions and decisions under uncertainty. Starting with the basics of Markov processes, the book gradually delves into more complex concepts and methods. The authors use clear language and illustrative examples, ensuring that even newcomers to the topic can grasp the material easily. A notable feature is the inclusion of exercises and problems, allowing readers to reinforce their understanding and test their skills—making it an excellent textbook for students and instructors alike. The topics covered are broad, including parameter estimation for Markov processes, maximum likelihood methods, Bayesian approaches, and more. The authors highlight the significance of Markov models across various scientific and technical disciplines, underscoring the book’s relevance not only for theorists but also for practitioners. Readers will discover how Markov models are applied in economics to analyze financial markets, in biology to model populations, and in engineering to optimize processes. *Markov Theory of Stochastic Process Estimation* will appeal not only to students and researchers but also to professionals seeking innovative approaches to complex problems. If you work in data analysis, statistics, or probability theory, this book will become an indispensable source of knowledge and inspiration. It’s also highly relevant for those interested in modern machine learning and artificial intelligence, many of which are based on Markov principles. The authors’ writing style is clear and logical, making it easy for readers to follow their reasoning. Both Mikhail Semyonovich Yarlykov and Mikhail Arkadyevich Mironov are experienced experts in probability theory, and their expertise shines through in every chapter. Their previous publications have been well received in academic circles, and this edition continues their tradition of high-quality, thorough analysis. In short, *Markov Theory of Stochastic Process Estimation* is more than just a textbook—it's a comprehensive guide for anyone looking to deepen their understanding of probability and statistics. It opens the door to the world of Markov processes, offering readers a unique opportunity not only to learn the theory but also to master its practical applications. If you’re seeking authoritative literature on stochastic process estimation, this book will be your reliable companion in exploring this fascinating field.