24 Best Books on Stochastic Process

“An Introduction to Continuous-Time Stochastic Processes (Modeling and Simulation in Science, Engineering and Technology)” Book Review: The intended audience for this book encompasses mathematicians, researchers, practitioners, as well as students studying mathematical finance, biomathematics, biotechnology, and engineering. The book provides comprehensive coverage of continuous-time stochastic processes, stochastic integrals, and stochastic differential equations. Additionally, it explores Markov processes, population dynamics, epidemics, and agent-based models, while also containing valuable insights on Levy processes, fractionation Brownian motion, ergodic theory, and Karhunen-Loeve expansion. Furthermore, it delves into the Smoluchowski approximation of Langevin systems.

“Simulation of Stochastic Processes with Given Accuracy and Reliability” Book Review: The book offers advantages to both mathematicians and practitioners alike, as it delves into the topics of sub-gaussian and square gaussian random variables. It also provides valuable insights into accurately and reliably measuring functional spaces. In addition, readers can benefit from the various simulation methods of stochastic processes outlined in the book.

“The Little Book of Probability: Essentials of Probability for Stochastic Processes and Simulation” Book Review: This book is a concise and accessible guide to probability theory. It covers fundamental concepts such as sample space, events, probability laws, conditional probability, and Bayes’ theorem. The book also introduces random variables, discrete and continuous probability distributions, and stochastic processes. With its clear explanations, numerous examples, and exercises, this book is an excellent resource for anyone seeking a solid foundation in probability theory for stochastic processes and simulation.

“Introduction to Stochastic Processes with R” Book Review: This book is advantageous to mathematicians, statisticians, as well as science, technology, engineering, and mathematics (STEM) students. It offers practical applications of probability theory in both natural and social sciences, covering topics such as Markov chains, Monte Carlo simulations, random walks on graphs, card shufflings, and Black-Scholes option pricing. Additionally, it delves into cryptography, martingales, and stochastic calculus. The book concludes each chapter with numerous exercise questions and examples to aid readers in their understanding.

“Stochastic Processes in Polymeric Fluids: Tools and Examples for Developing Simulation Algorithms” Book Review: This book presents a mathematical perspective on stochastic processes and is a valuable resource for anyone working in the fields of engineering or natural sciences. With a focus on applications of stochastic processes to molecular theories of polymeric fluids, the text offers a clear understanding of polymer dynamics. Additionally, the book includes numerous examples that are designed to enhance comprehension of the subject matter.

“Simulation and Chaotic Behavior of Alpha-stable Stochastic Processes: 178” Book Review: This book is a comprehensive guide to the simulation of alpha-stable stochastic processes. The book includes detailed explanations of the theory and application of these processes, as well as their chaotic behavior. It covers topics such as simulation techniques, statistical properties of alpha-stable distributions, and applications in finance, physics, and engineering. The book is aimed at researchers and graduate students in the fields of applied mathematics, physics, and engineering.

“Introduction to Rare Event Simulation (Springer Series in Statistics)” Book Review: The book presents a comprehensive guide on rare event simulation, covering various aspects of the subject. It includes a range of informative diagrams that facilitate the understanding of the topic. Furthermore, the book illustrates the diverse applications of rare event simulation in areas such as statistics, telecommunication, and queuing systems. Additionally, it delves into the methodology and principles of large deviation theory, providing a detailed analysis of the topic.

“Stochastic Processes: Modeling and Simulation (Handbook of Statistics)” Book Review: The book is a valuable resource for students, teachers, researchers, and practitioners alike. It presents the most recent advancements in stochastic processes, including random graphs, reliability, epidemic modeling, self-similar processes, and empirical processes. Furthermore, it provides an overview of time-series models, extreme value theory, the use of Markov chains, and Monte Carlo techniques in modeling. The book also features numerous applications of stochastic processes in various fields, including engineering, telecommunications, biology, astronomy, and chemistry.

“Affine Diffusions and Related Processes: Simulation, Theory and Applications (Bocconi & Springer Series)” Book Review: This book is an ideal resource for individuals engaged in the field of mathematical finance, including students and researchers. It delves into various topics such as affine diffusions in Ornstein-Uhlenbeck processes, Wishart processes, and Wright-Fisher processes. Furthermore, the book provides a comprehensive explanation of how to simulate these processes, making it an invaluable reference for those seeking to expand their knowledge in this area.

“Linear Programming” Book Review: This book is a comprehensive guide on linear programming techniques. It covers topics such as basic linear algebra concepts, simplex method, duality theory, sensitivity analysis, integer programming, game theory, and network flows. The book provides numerous examples and exercises to help readers understand and apply the concepts. Additionally, it explores the applications of linear programming in various fields like economics, engineering, and operations research. This book is a valuable resource for students and professionals looking to deepen their knowledge of linear programming.

“Applied Mathematical Programming” Book Review: This book presents a comprehensive introduction to mathematical programming, encompassing topics such as sensitivity analysis, linear programming duality, practical mathematical programming applications, and strategic planning integration. Additionally, the book provides detailed explanations of linear program solving, integer programming, vectors and matrices, as well as linear programming in matrix form.

“Exploring Interior Point Linear Programming, Algorithms, and Software: Book Review: This book offers an overview of linear programming problem modeling, beginning with an introduction to linear programming. It encompasses a comprehensive study of linear optimization problem modeling, as well as the simplex algorithm, primal algorithm, dual algorithm, primal-dual algorithm, and implementation. Additionally, the book includes problem sets with accompanying solutions.

“Applied Stochastic Processes” Book Review: This is a comprehensive textbook for graduate-level courses in stochastic processes. The book covers the fundamental concepts of probability theory and stochastic processes, including Markov chains, renewal processes, Poisson processes, Brownian motion, and queueing theory. It also provides in-depth coverage of applications of stochastic processes in areas such as finance, engineering, operations research, and computer science. The book includes numerous examples, exercises, and computer simulations to aid student’s understanding. This is a useful resource for anyone looking to develop a deep understanding of stochastic processes and their applications.

“A Course in Applied Stochastic Processes” Book Review: This book is a comprehensive textbook on stochastic processes. The book is designed for graduate and advanced undergraduate students in mathematics, statistics, and engineering. The book covers topics such as Poisson processes, Markov chains, Brownian motion, stochastic calculus, and queuing theory. The authors provide numerous examples and exercises throughout the text to help students understand the material. This book is an ideal resource for those seeking a deeper understanding of stochastic processes and their applications.

“Basics of Applied Stochastic Processes (Probability and Its Applications)” Book Review: The book offers an in-depth exploration of the structure and fundamental characteristics of stochastic processes. It delves into a range of topics, including equilibrium distributions, strong laws of large numbers, and functional central limit theorems for cost and performance parameters. The text is replete with examples and exercises to aid understanding. Additionally, it offers comprehensive coverage of stochastic networks, spatial and space-time Poisson processes, queueing, reversible processes, and simulation.

“Stochastic Processes (Classics in Applied Mathematics)” Book Review: The book provides an introduction to the uses of probability models in various scientific fields, including medicine, biology, physics, economics, and psychology. Its aim is to solve problems related to stochastic processes. Additionally, it covers the fundamentals of probability theory and mathematical analysis, offering numerous examples to enhance the reader’s understanding.

“Applied Stochastic Processes” Book Review: This book is a compilation of papers that cover stochastic processes, stochastic equations, and their practical applications. The book examines stochastic systems that involve randomness in the system, the way existing cells are constructed, and the detection of distributed and fluctuating targets. One of the papers in the book suggests that only the average properties of molecules can be measured using test tubes.

“Statistical Inference for Discrete Time Stochastic Processes” Book Review: The book is focused on statistical inference in discrete time stochastic processes with a stationary nature. It commences by presenting an overview of martingales and strong mixing sequences, followed by an introductory section that covers Markov chains, Raftery’s Mixture, transition density model, and Hidden Markov chains. The book provides a lucid explanation of how ARMA models can be extended with various stationary distributions, such as binomial, Poisson, geometric, exponential, gamma, Weibull, Lognormal, inverse, Gaussian, and Cauchy. Towards the end, the book emphasizes the practical applications of semi-parametric estimation techniques, such as conditional least squares and estimating functions, in stochastic models. This book is an excellent reference for researchers involved in discrete time stochastic processes and inference.

Book Review: This book explores the stochastic processes present in probabilistic systems and effectively illustrates the concepts of discrete stochastic processes. By applying the theory of stochastic processes in engineering, science, and operations research, the book provides numerous examples that showcase the structure of stochastic processes and their impact on real-world systems. Furthermore, the book utilizes mathematical proofs and explanations to clarify the presented ideas. It is an invaluable resource for students in graduate and undergraduate courses studying engineering and operations research.

Book Review: This graduate course book is useful for a single semester. However, familiarity with elementary stochastic process theory is necessary before delving into it. The book provides a solid mathematical foundation for understanding the various aspects of Kalman filtering. It comprises chapters on state space theory, signal (random process) theory, statistical estimation theory, and the mathematical framework that underpins Kalman filtering. The book concludes with a chapter on likelihood ratios, which emphasizes the significance of the Kalman filter formulation. Discrete time models are utilized throughout the book to elucidate various concepts.

Book Review: The book provides valuable information on linear and nonlinear filtering theories, making it a valuable resource for engineering students. To benefit from this book, readers should have a strong understanding of advanced calculus, ordinary differential equation theory, and matrix analysis. The book covers both theoretical concepts and practical applications, including the modeling of dynamic systems using finite-dimensional Markov processes and outputs from stochastic differential and difference equations. Additionally, the author addresses filtering, prediction, and smoothing problems and offers mathematical solutions to nonlinear filtering issues, as well as the development of approximate nonlinear filters.