32 Best Books on Stochastic Models

We have compiled a list of the Best Reference Books on Stochastic Models, which are used by students of top universities, and colleges. This will help you choose the right book depending on if you are a beginner or an expert. Here is the complete list of Stochastic Models Books with their authors, publishers, and an unbiased review of them as well as links to the Amazon website to directly purchase them. If permissible, you can also download the free PDF books on Stochastic Models below.

1. Introduction to Stochastic Models

 
1."Introduction to Stochastic Processes" by E Cinlar
“Introduction to Stochastic Processes” Book Review: This book is an ideal resource for students, professionals, and researchers who wish to expand their knowledge of stochastic processes. It provides a comprehensive overview of the fundamental concepts and theories in this field, including probability spaces, random variables, expectations, independence, Bernoulli processes, sums of independent random variables, Poisson processes, Markov chains and processes, and renewal theory. The book is well-organized, featuring clear and concise explanations, as well as numerous examples, illustrations, and exercises.

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2."Stochastic Modeling and the Theory of Queues" by R W Wolff
Book Review: This book provides a contemporary insight into the applied aspects of stochastic processes and queuing theory, with particular emphasis on time averages. It discusses the significance of priorities, queue pooling, bottlenecks and related concepts. The material is appropriate for advanced courses in queuing theory, such as those in the fields of operations research, computer science, statistics, and industrial engineering. The book also covers queues and stochastic processes, as well as renewal theory, renewal theorems, and regenerative processes.

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3."Introduction To Stochastic Models" by Nikolaos Limnios
“Introduction To Stochastic Models” Book Review: This informative book is recommended for students, researchers in applied sciences, and anyone seeking an introduction to stochastic models. It covers the application of stochastic models in various fields such as physics, engineering, biology, genetics, economics, and social sciences. The book includes discussions on important topics such as Markov and semi-Markov models, Poisson and renewal processes, branching processes, Ehrenfest models, genetic models, optimal stopping, reliability, reservoir theory, storage models, and queuing systems.

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4."INTRODUCTION TO STOCHASTIC MODELS" by D’Marreio Brooks
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5."An Introduction to Differential Equations: Stochastic Modeling, Methods and Analysis" by Anil G Ladde and G S Ladde
“An Introduction to Differential Equations: Stochastic Modeling, Methods and Analysis” Book Review: The book is primarily designed for undergraduate and graduate students studying stochastic modeling and applied mathematics, but it will also benefit researchers in these fields. It covers various topics, including the basics of stochastic processes and Ito-Doob stochastic calculus, first-order differential equations, first-order nonlinear differential equations, first-order systems of linear differential equations, and higher-order differential equations. The book covers all the significant concepts and techniques related to these topics.

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6."An Introduction to Stochastic Modeling" by Mark A Pinsky and Samuel Karlin
“An Introduction to Stochastic Modeling” Book Review: The book is a suitable reference for undergraduate and graduate students studying stochastic processes and modeling. It commences with an introduction to stochastic modeling and then progresses to explain standard concepts and methodologies in the field. The book highlights various real-life and biological applications of stochastic processes. It includes exercises for self-assessment at the end of each chapter. The content is up-to-date, with new chapters and problems added to the latest edition.

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7."Elementary Introduction to Stochastic Interest Rate Modeling" by Nicolas Privault
“Elementary Introduction to Stochastic Interest Rate Modeling” Book Review: This book is aimed at advanced undergraduate and graduate level students and focuses on interest rate modeling and pricing related derivatives. It covers topics such as stochastic interest rate models, standard short rate models, forward rate models, BGM model, pricing of related derivatives, and calibration approaches. The book also includes a chapter on credit risk and provides exercises with clear solutions at the end of each chapter.

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8."Introduction to Stochastic Networks" by Richard Serfozo
“Introduction to Stochastic Networks” Book Review: This book is primarily intended for graduate students and researchers in engineering, science, and mathematics. It begins with a description of Jackson networks and goes on to explain spatial queuing systems. The book covers a range of topics, including basic stochastic network processes, reversible Markov processes, palm probabilities for stationary systems, little laws for queuing systems, and space-time Poisson processes. The book also features expressions for the equilibrium probability distribution of the numbers of units at the stations, as well as real-analysis and recent developments in stochastic networks.

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9."Stochastic Models, Estimation and Control Vol.I" by P S Maybeck
“Stochastic Models, Estimation and Control: Volume 1” Book Review: This self-contained book aims to provide a complete understanding of the fundamentals of stochastic processes, estimation, and control. It includes mathematical results as well as implementation of estimation and control algorithms in this field. The book covers deterministic system models, stochastic processes and linear dynamic system models, optimal filtering and linear system models, probability theory and static models, design and performance analysis of Kalman filters, and square root filtering. The book is intended for engineers, as well as students and researchers studying in engineering courses, and includes numerous mathematical proofs and examples.

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2. Selected Application of Stochastic Models

 
1."Dynamic Programming and Optimal Control" by D Bertsikas
Book Review: This book focuses on important concepts such as modeling, conceptualization, finite horizon problems, and mathematical analysis and computation. It provides up-to-date information about large scale dynamic programming and reinforcement learning, and can be used for optimal control, Markovian decision problems, and combinatorial problems. The book emphasizes basic unifying themes and conceptual foundations, and addresses practical applications of methodology, including the use of approximations.

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2."Options Futures and other Derivatives" by J C Hull
Book Review: This book explores the latest advancements in applied stochastic processes and queuing theory, with a strong focus on time averages and long run behavior. It also demonstrates the practical effects of priorities, pooling of queues, and bottlenecks. The book develops queuing theory models in mathematical language and applies them to various engineering problems. It is ideal for graduate level courses in queuing theory in operations research, computer science, and statistics departments.

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3."Stochastic Modeling and Theory of Queues" by R W Wolff
Book Review: This book bridges the gap between theoretical and practical methods of stochastic modeling and the theory of queues. It provides an introduction to futures and options markets and is suitable for readers with a limited background in mathematics. The book includes a new chapter on securitization and credit crisis, as well as a discussion on commodity prices, modeling, and commodity derivatives.

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4."Stochastic Volatility: Selected Readings (Advanced Texts in Econometrics)" by Neil Shephard
“Stochastic Volatility: Selected Readings (Advanced Texts in Econometrics)” Book Review: This book enhances the reader’s knowledge of financial volatility, covering stocks, bonds, currencies, and range from 1973 to 2001. It presents a clear introduction to stochastic volatility and addresses major issues involved in volatility. The book also includes an invaluable survey on the state-of-the-art of SV modeling in finance, and is useful for students and professionals dealing with volatility modeling.

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5."Selected Papers of Frederick Mosteller (Springer Series in Statistics)" by Stephen E Fienberg and David C Hoaglin
“Selected Papers of Frederick Mosteller (Springer Series in Statistics)” Book Review: This book is a collection of 40 original and influential papers by Frederick Mosteller that provide information about statistics. It covers all the concepts and issues related to statistics and highlights the applications of statistics in various fields. The book also features many statistical problems, and will be useful for statisticians and new generations of researchers.

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6."Selected Topics in Cancer Modeling: Genesis, Evolution, Immune Competition, and Therapy" by Nicola Bellomo and Elena de Angelis
“Selected Topics in Cancer Modeling: Genesis, Evolution, Immune Competition, and Therapy” Book Review: This book presents modern mathematical methods and tools for modeling and analyzing cancer phenomena, covering topics such as stochastic evolutionary models of cancer initiation and progression, tumor cords and their response to anticancer agents, genetic and epigenetic pathways to colon cancer, nonlinear modeling and simulation of tumor growth, and immune competition in tumor progression and prevention. The book is useful for researchers, practitioners, and graduate students in applied mathematics, mathematical biology, and related fields.

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7."Evaluation of Statistical Matching and Selected SAE Methods: Using Micro Census and EU-SILC Data" by Verena Gissing
“Evaluation of Statistical Matching and Selected SAE Methods: Using Micro Census and EU-SILC Data” Book Review: This book aims to find the best method for the estimation of poverty in terms of small bias and small variance with the aid of a simulated artificial “close-to-reality” population. It covers topics such as regression models, small area methods, statistical matching, and bootstrap methods, and includes applications to poverty estimation using EU-SILC and micro census data. The book evaluates and compares statistical matching and selected SAE methods, and will be helpful for researchers, students, and practitioners in the fields of statistics, official statistics, and survey statistics.

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8."Models and Methods in the Philosophy of Science: Selected Essays" by Patrick Suppes
“Models and Methods in the Philosophy of Science: Selected Essays” Book Review: This book covers general methodology, formal and axiomatic methods in science, and the probabilistic theory of causality. It also covers topics such as probability, measurement, foundations of physics, quantum mechanics, and foundations of psychology, and highlights the latest roles of formal methods. The book is primarily for philosophers of science, scientists concerned with the methodology of social sciences, and mathematical psychologists interested in theories of learning, perception, and measurement.

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9."Stochastic Models, Information Theory, and Lie Groups" by Gregory S Chirikjian
“Stochastic Models, Information Theory, and Lie Groups” Book Review: This book is aimed at advanced undergraduate and graduate students, researchers, and practitioners in applied mathematics, physical sciences, and engineering. It offers a clear introduction to the fundamentals of continuous-time stochastic processes, as well as in-depth coverage of differential geometry, the probabilistic foundations of information theory, stochastic geometry, geometric aspects of communications and coding theory, multivariate statistical analysis, and error propagation on Lie groups. The book also highlights the applications of stochastic processes in biomolecular statistical mechanics and information-driven motion in robotics. Numerous exercises, examples, and modern problems related to stochastic processes on Lie groups are included.

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10."Stochastic Parameter Regression Models" by Paul Newbold
“Stochastic Parameter Regression Models” Book Review: This book provides a technical and advanced introduction to stochastic parameter regression models that explore time-varying relationships independent of position, motion, and cause. It is intended for professionals, students, and researchers who are familiar with standard regression models and seek to understand methods that address relationships that evolve gradually over time.

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11."Modelling Extremal Events: for Insurance and Finance (Stochastic Modelling and Applied Probability)" by Paul Embrechts and Claudia Klüppelberg
“Modelling Extremal Events: for Insurance and Finance (Stochastic Modelling and Applied Probability)” Book Review: This book is a comprehensive guide on modelling extreme events in the field of insurance and finance. The book covers topics like statistical modelling of extreme events, multivariate extremes, risk measures, and dependence modelling. The authors provide a detailed discussion of tail modelling, copulas, and risk management. The book is a useful resource for advanced undergraduate and graduate level students, as well as researchers and practitioners in insurance and finance.

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3. Stochastic Modelling of Materials

 
1."Heavy Traffic Analysis of Controlled Queueing and Communication Networks (Stochastic Modelling and Applied Probability)" by Harold Kushner
“Heavy Traffic Analysis of Controlled Queueing and Communication Networks (Stochastic Modelling and Applied Probability)” Book Review: This book focuses on the traffic analysis of controlled queueing and communication networks and covers applications of stochastic networks for both controlled and uncontrolled systems. It also includes different models on stochastic differential equations and discusses scheduling, admissions control, and polling. In addition, it covers converging methods, limit processes, and ergodic problems. This book is aimed at engineering students, researchers, and scientists.

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2."Modelling Stochastic Fibrous Materials with Mathematica® (Engineering Materials and Processes)" by William Wyatt Sampson
“Modelling Stochastic Fibrous Materials with Mathematica® (Engineering Materials and Processes)” Book Review: This book discusses the use of electrospun fibrous materials, including carbon fibrous materials in fuel cells, as well as the structure of stochastic fibrous materials. It explains the techniques of statistical geometry and probabilistic modeling and includes solved examples, exercises, and short questions at the end of each chapter. This book is designed for engineering students, researchers, and scientists.

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3."Fundamentals of Stochastic Filtering (Stochastic Modelling and Applied Probability)" by Alan Bain and Dan Crisan
“Fundamentals of Stochastic Filtering (Stochastic Modelling and Applied Probability)” Book Review: This book covers the treatment of the non-linear stochastic filtering problem and includes theoretical analysis of numerical methods, as well as measure theory, probability theory, and basics of stochastic processes. It also includes solved examples, exercises, and short questions at the end of each chapter. This book is designed for engineering students, researchers, and scientists.

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4."Discrete-Time Markov Chains: Two-Time-Scale Methods and Applications (Stochastic Modelling and Applied Probability)" by G George Yin and Qing Zhang
“Discrete-Time Markov Chains: Two-Time-Scale Methods and Applications (Stochastic Modelling and Applied Probability)” Book Review: This book discusses two-time-scale Markov chains in discrete time and their applications in optimization and control of complex systems in manufacturing. It includes both theoretical and practical aspects of discrete time Markov chains, along with solved examples, exercises, and short questions at the end of each chapter. This book is designed for physicists, researchers, students, and scientists.

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5."Controlled Markov Processes and Viscosity Solutions (Stochastic Modelling and Applied Probability)" by Wendell H Fleming and Halil Mete Soner
“Controlled Markov Processes and Viscosity Solutions (Stochastic Modelling and Applied Probability)” Book Review: This book covers the theory of viscosity solutions, introduction to optimal stochastic control for continuous time Markov processes, and dynamic programming for deterministic optimal control problems. It focuses on the fundamental equation of dynamic programming and includes both theoretical and practical aspects of Markov processes, along with solved examples, exercises, and short questions at the end of each chapter. This book is designed for physicists, researchers, students, and scientists.

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6."Stochastic Modelling and Filtering" by Alfredo Germani
“Stochastic Modelling and Filtering” Book Review: This book focuses on stochastic modeling and filtering, as well as control and information science, and covers both theoretical and practical applications. It includes solved examples, exercises, and short questions at the end of each chapter and is designed for students, researchers, and scientists.

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7."Molecular Dynamics: With Deterministic and Stochastic Numerical Methods (Interdisciplinary Applied Mathematics)" by Ben Leimkuhler and Charles Matthews
“Molecular Dynamics: With Deterministic and Stochastic Numerical Methods (Interdisciplinary Applied Mathematics)” Book Review: This book focuses on molecular dynamics, algorithms used for molecular dynamics simulation, and the basic theory of Hamiltonian mechanics and stochastic differential equations. It includes both theoretical and practical aspects of molecular dynamics, along with solved examples, exercises, and short questions at the end of each chapter. This book is designed for students, researchers, and scientists.

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8."Hybrid Switching Diffusions: Properties and Applications (Stochastic Modelling and Applied Probability)" by Chao Zhu and G George Yin
“Hybrid Switching Diffusions: Properties and Applications (Stochastic Modelling and Applied Probability)” Book Review: This book discusses the development of regime-switching diffusions, including switching diffusion equations, ergodicity, and two-time-scale processes. It focuses on hybrid switching diffusions and includes solved examples, exercises, and short questions at the end of each chapter. This book is designed for researchers, engineers, and scientists.

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9."Turbulence in Fluids: Stochastic and Numerical Modelling (Mechanics of Fluids and Transport Processes)" by Marcel Lesieur
“Turbulence in Fluids: Stochastic and Numerical Modelling (Mechanics of Fluids and Transport Processes)” Book Review: This book explains the concepts of turbulence in fluids, focusing on ocean dynamics and geostrophic theory, as well as the theories and applications of fluid dynamics and rotational fluid dynamics. It includes solved examples, exercises, and short questions at the end of each chapter and is designed for students, researchers, and scientists in mechanical engineering.

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10."Stochastic Modeling of Microstructures (Modeling and Simulation in Science, Engineering and Technology)" by Kazimierz Sobczyk and David J Kirkner
“Stochastic Modeling of Microstructures (Modeling and Simulation in Science, Engineering and Technology)” Book Review: This book covers media microstructure, statistical inference, probability and random variables, and microstructural modeling and stochastic process, as well as the practical applications of stochastic modeling of microstructures. It includes solved examples, exercises, and short questions at the end of each chapter and is designed for students, chemists, researchers, and scientists.

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11."Stochastic Models, Estimation and Control Vol.II" by P S Maybeck
“Stochastic Models, Estimation and Control: Volume 2” Book Review: This book expands on the concepts presented in Volume 1, covering optimal smoothing and compensation for linear model inadequacies in addition to filtering. It also explores the development of nonlinear stochastic system models to aid in the design of practical nonlinear estimation algorithms. The extended Kalman filter algorithm is introduced and an initial study is discussed. The book also includes a description of adaptive estimation based on linear models with embedded uncertain parameters. Mathematical proofs and examples are plentiful throughout the book. A solid foundation in advanced calculus, differential equations, and basic vector and matrix analysis at an engineering level is a prerequisite for readers. This book is intended for engineers, students, and researchers studying engineering courses.

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12."Stochastic Models, Estimation and Control Vol.III" by P S Maybeck
“Stochastic Models, Estimation and Control: Volume 3” Book Review: This book is the next installment in a series that includes Volumes 1 and 2. It introduces the fundamentals of stochastic control and dynamic programming as a means of synthesizing optimal stochastic control laws. The book then covers LQG synthesis of controllers, which utilizes linear system models, Gaussian noise models, and quadratic cost criteria to define optimality. Practical nonlinear controllers are also developed using the concepts introduced in Volumes 1 and 2. Numerous illustrations and examples are included to aid in understanding. Advanced knowledge of calculus, differential equations, and basic vector and matrix analysis at an engineering level is necessary to fully comprehend the content of this book. It is intended for engineers, students, and researchers studying engineering courses.

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We have put a lot of effort into researching the best books on Stochastic Models and came out with a recommended list and their reviews. If any more book needs to be added to this list, please email us. We are working on free pdf downloads for books on Stochastic Models and will publish the download link here. Fill out this Stochastic Models books pdf download" request form for download notification.

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