We have compiled the list of Best Reference Books on Stochastic Models subject. These books are used by students of top universities, institutes and colleges. Here is the full list of best books on Stochastic Models along with reviews.
Kindly note that we have put a lot of effort into researching the best books on Stochastic Models subject and came out with a recommended list of best books. The table below contains the Name of these best books, their authors, publishers and an unbiased review of books on “Stochastic Models” as well as links to the Amazon website to directly purchase these books. As an Amazon Associate, we earn from qualifying purchases, but this does not impact our reviews, comparisons, and listing of these top books; the table serves as a ready reckoner list of these best books.
List of Stochastic Models Books with author’s names, publishers, and an unbiased review as well as links to the Amazon website to directly purchase these books.
 Introduction to Stochastic Models
 Selected Application of Stochastic Models
 Stochastic Modelling of Materials
1. Introduction to Stochastic Models
1. “Introduction to Stochastic Processes” by E Cinlar
“Introduction to Stochastic Processes” Book Review: The book is highly recommended for the students, professionals and researchers who want to gain knowledge in the field of ‘Stochastic Processes’. All the basic concepts and theory of the preceding field is described in this book. It covers topics like probability spaces and random variables, expectations and independence, Bernoulli processes and sums of independent random variables, Poisson processes, Markov chains and processes, and renewal theory. The content of the book is precise, clear, and wellstructured. It features many exercises, examples and illustrations.


2. “Stochastic Modeling and the Theory of Queues” by R W Wolff
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Book Review: This book contains uptodate information on applied stochastic processes and queuing theory with major stress on time averages. The book also demonstrates the concepts of priorities, queue pooling, bottlenecks and many more. This book is suitable for graduate courses in queuing theory in the field of operations research, computer science, statistics and industrial engineering. The book also deals with queues and stochastic processes. It also contains details about renewal theory, renewal theorems and regenerative processes.


3. “Introduction To Stochastic Models” by Nikolaos Limnios
“Introduction To Stochastic Models” Book Review: The book will be useful for students and researchers in applied sciences or anyone seeking an introduction to ‘Stochastic Models’. This book is an informative text, which describes how the stochastic models are encountered in applied sciences and techniques such as physics, engineering, biology and genetics, economics and social sciences. The book contains topics like markov and semimarkov models, poisson, renewal processes, branching processes, ehrenfest models, genetic models, optimal stopping, reliability, reservoir theory, storage models, and queuing systems.


4. “INTRODUCTION TO STOCHASTIC MODELS” by D’Marreio Brooks  
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 basically for undergraduate and graduate students of stochastic modeling and applied mathematics courses, but will also be helpful for the researchers of these courses. It features topics like elements of stochastic processes and ito–doob stochastic calculus, firstorder differential equations, firstorder nonlinear differential equations, firstorder systems of linear differential equations, higherorder differential equations. The book contains all the important concepts and methodologies of the given topics.


6. “An Introduction to Stochastic Modeling” by Mark A Pinsky and Samuel Karlin
“An Introduction to Stochastic Modeling” Book Review: The book can be referred by the students for their undergraduate and graduate courses related to stochastic processes and modeling. The book starts with introduction to stochastic modeling, and further describes standard concepts and methods of stochastic modeling. Many reallife and biological applications of stochastic processes are highlighted in this book. Each chapter is followed by exercises for selfassessment. The content of this book is fully updated, thus containing new chapters and problems.


7. “Elementary Introduction to Stochastic Interest Rate Modeling” by Nicolas Privault
“Elementary Introduction to Stochastic Interest Rate Modeling” Book Review: The book can be referred by advanced undergraduate and graduate level students. It is majorly based on ‘interest rate modeling’ and ‘pricing of related derivatives’. The topics like stochastic interest rate models, standard short rate models, forward rate models, pricing of related derivatives, Bgm model, and an approach to its calibration are also covered in this book. The book also features a chapter on ‘credit risk’. Each chapter is ended with exercises along with their clear solutions.
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8. “Introduction to Stochastic Networks” by Richard Serfozo
“Introduction to Stochastic Networks” Book Review: The book is basically for graduate students and researchers in engineering, science and mathematics. It starts with a description of Jackson networks and then explains spatial queuing systems. It describes topics like basic stochastic network processes, reversible markov processes, palm probabilities for stationary systems, little laws for queuing systems, and spacetime Poisson processes. The book features expressions for the equilibrium probability distribution of the numbers of units at the stations. It also highlights realanalysis and recent developments in the stochastic networks.


9. “Stochastic Models, Estimation and Control Vol.I” by P S Maybeck
“Stochastic Models, Estimation and Control: Volume 1” Book Review: This selfcontained book aims to develop a complete understanding of the fundamentals of stochastic processes, estimation, and control. Both the mathematical results as well as implementation of estimation and control algorithms in this field are included. Spanning over seven chapters, 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. Numerous mathematical proofs and examples are also provided. This book is intended for engineers as well as students and researchers studying in engineering courses.


2. Selected Application of Stochastic Models
1. “Dynamic Programming and Optimal Control” by D Bertsikas
Book Review: This book is focused towards modeling, conceptualization, finite horizon problems and many other important concepts. The other part of the book deals with mathematical analysis and computation and presents uptodate information about large scale dynamic programming and reinforcement learning. This book can also be used for optimal control, markovian decision problems and combinatorial problems. The book also stresses on basic unifying themes and conceptual foundations. The book also addresses practical application of methodology along with the use of approximations.


2. “Options Futures and other Derivatives” by J C Hull
Book Review: This book covers the latest advancements in the field of applied stochastic processes and queuing theory with a strong focus on time averages and long run behavior. The book simultaneously also illustrates the practical effects like priorities, pooling of queues and bottlenecks. The book also develops queuing theory models in mathematical language and applies it to many engineering problems. This book is suitable for courses in queuing theory in operations research, computer science and statistics departments at the graduate level.


3. “Stochastic Modeling and Theory of Queues” by R W Wolff
Book Review: This book bridges the gap between the theoretical and practical methods of stochastic modeling and theory of queues. The book also provides introduction to futures and options markets and is very suitable for people with a limited background in mathematics. The book also contains a new chapter on securitization and credit crisis and additionally the book also includes a discussion on way commodity prices where modeling and commodity derivatives are valued.


4. “Stochastic Volatility: Selected Readings (Advanced Texts in Econometrics)” by Neil Shephard
“Stochastic Volatility: Selected Readings (Advanced Texts in Econometrics)” Book Review: The book will enhance the reader’s knowledge of financial volatility. Stocks, bonds, currencies and range from 1973 to 2001 are covered in this book. It presents a clear introduction of stochastic volatility along with all the major issues involved in volatility. An invaluable survey on the stateoftheart of SV modelling in finance is featured in this book. The book will be useful for students and professionals dealing with volatility modeling.


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: The book is a collection of 40 original and influential papers of Frederick Mosteller, which contains information about statistics. All the concepts and issues related to statistics are described in this text. The applications of statistics in various fields are highlighted in this book. The book also features many statistical problems. This piece of writing will be useful for statisticians and new generations of researchers.


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: The book presents modern mathematical methods and tools for modeling and analyzing cancer phenomena. Topics like 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, tumor cords and their response to anticancer agents, mathematical modeling of breast carcinogenesis, and immune competition in tumor progression and prevention are discussed in this book. The book will be useful for researchers, practitioners, and graduate students in applied mathematics, mathematical biology, and related fields.


7. “Evaluation of Statistical Matching and Selected SAE Methods: Using Micro Census and EUSILC Data” by Verena Gissing
“Evaluation of Statistical Matching and Selected SAE Methods: Using Micro Census and EUSILC Data” Book Review: The book aims in finding the best method for the estimation of poverty in terms of small bias and small variance with the aid of a simulated artificial “closetoreality” population. The chapters of this book cover topics like regression models, small area methods, statistical matching, and bootstrap methods. The applications to poverty estimation using EUSILC and micro census data are mentioned in this book. In this text, statistical matching and selected SAE methods are evaluated and compared. The book will be helpful for researchers, students, and practitioners in the fields of statistics, official statistics, and survey statistics.


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: Initially, the book covers general methodology, formal and axiomatic methods in science, and probabilistic theory of causality. Moving forward topics like probability, measurement, foundations of physics, quantum mechanics, and foundations of psychology are discussed. The latest roles formal methods are highlighted. The book is basically for the philosophers of science, scientists concerned with the methodology of the social sciences, and mathematical psychologists interested in theories of learning, perception and measurement.


9. “Stochastic Models, Information Theory, and Lie Groups” by Gregory S Chirikjian
“Stochastic Models, Information Theory, and Lie Groups” Book Review: The book is for advanced undergraduate and graduate students, researchers, and practitioners working in applied mathematics, the physical sciences, and engineering. The fundamentals of continuoustime stochastic processes are clearly mentioned. Along with it, differential geometry, and the probabilistic foundations of information theory, stochastic geometry, geometric aspects of the theory of communications and coding, multivariate statistical analysis, and error propagation on Lie groups are thoroughly explained. The applications of stochastic processes like, biomolecular statistical mechanics and informationdriven motion in robotics are highlighted in this text. The book features various exercises, examples, and modern problems related to stochastic processes on Lie groups.


10. “Stochastic Parameter Regression Models” by Paul Newbold
“Stochastic Parameter Regression Models” Book Review: The book presents technical and advanced introduction to stochastic parameter regression models. The models examine timevarying relationships irrespective of position, motion, and cause. This book will be useful for the students, professionals, and researchers who are working on standard regression models and wish to understand methods which deal with relationships that vary slowly over time.


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: The book provides a mix of theory and applications with a large number of illustrations. The book also provides a large number of real life examples and it also gives a large number of shapes and distributions. This book is mainly designed for probabilists and statisticians.


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 is designed for the engineering students, researchers and scientists. It basically deals with the traffic analysis of controlled queueing and communication networks. It also explains the applications of stochastic networks for both controlled and uncontrolled systems. Different models on stochastic differential equations are also explained. The topics which are covered are scheduling, admissions control and polling. It also focuses on the converging methods, limit processes and ergodic problems.


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 is designed for the engineering students, researchers and scientists. It discusses the use of electrospun fibrous materials. It discusses the carbon fibrous materials in fuel cells. The structure of stochastic fibrous materials is also covered in this book. It also explains the techniques of statistical geometry and probabilistic modelling. It consists of solved examples, exercises and short questions at the end of the chapter.


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 is designed for the engineering students, researchers and scientists. It discusses the treatment of the nonlinear stochastic filtering problem. The theoretical analysis of numerical methods is also covered in this book. It also focuses on the measure theory, probability theory and the basics of stochastic processes. It consists of solved examples, exercises and short questions at the end of the chapter.


4. “DiscreteTime Markov Chains: TwoTimeScale Methods and Applications (Stochastic Modelling and Applied Probability)” by G George Yin and Qing Zhang
“DiscreteTime Markov Chains: TwoTimeScale Methods and Applications (Stochastic Modelling and Applied Probability)” Book Review: This book is designed for the physicists, researchers, students and scientists. It discusses the twotimescale markov chains in discrete time. It explains the applications in optimization, control of complex systems in manufacturing. Along with the theoretical portions it also covers the practical applications of the discrete time markov chains.It consists of solved examples, exercises and short questions at the end of the chapter.


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 is designed for the physicists, researchers, students and scientists. It covers the theory of viscosity solutions. It explains the introduction to optimal stochastic control for continuous time markov processes. It covers dynamic programming for deterministic optimal control problems. It focuses on the fundamental equation of dynamic programming. Along with the theoretical portions it also covers the practical applications of the markov processes. It also consists of solved examples, exercises and short questions at the end of the chapter.


6. “Stochastic Modelling and Filtering” by Alfredo Germani
“Stochastic Modelling and Filtering” Book Review: This book is designed for the students, researchers and scientists. It basically focuses on stochastic modelling and filtering. It also discusses control and information science. Along with the theoretical portions it also covers the practical applications of the stochastic modelling and filtering. It consists of solved examples, exercises and short questions at the end of the chapter.


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 is designed for the students, researchers and scientists. It basically deals with the molecular dynamics. It focuses on the algorithms used for molecular dynamics simulation. It also covers the understanding the foundations of numerical methods.It explains the basic theory of hamiltonian mechanics and stochastic differential equations. It consists of solved examples, exercises and short questions at the end of the chapter.


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 is designed for the researchers, engineers, scientists. It discusses the development of regimeswitching diffusions. The topics which are covered in this book are switching diffusion equations, ergodicity and twotimescale processes. It basically deals with hybrid switching diffusions. It consists of solved examples, exercises and short questions at the end of the chapter.


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 is designed for the students of mechanical engineering, researchers and scientists. It explains the concepts of turbulence in fluids. It focuses on ocean dynamics and geostrophic theory. It covers the theories of fluid dynamics. It discusses the applications of the rotational fluid dynamics. It consists of solved examples, exercises and short questions at the end of the chapter.


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 is designed for the students, chemist, researchers and scientists. The topics which are covered are media microstructure, statistical inference and probability and random variables. It also explains the microstructural modeling and stochastic process. The practical applications of the stochastic modeling of microstructures is also covered. It consists of solved examples, exercises and short questions at the end of the chapter.


11. “Stochastic Models, Estimation and Control Vol.II” by P S Maybeck
“Stochastic Models, Estimation and Control: Volume 2” Book Review: This book is built upon the foundations set in Volume 1 and explains optimal smoothing in addition to filtering and compensation of linear model inadequacies. The book also develops nonlinear stochastic system models to help elaborate the design of practical nonlinear estimation algorithms. An initial study of the important extended Kalman filter algorithm is also discussed. A description of adaptive estimation based upon linear models where uncertain parameters are embedded is also included. Numerous mathematical proofs and examples are also provided. Prerequisite knowledge of advanced calculus, differential equations, basic vector and matrix analysis on an engineering level is required. This book is intended for engineers as well as students and researchers studying in engineering courses.


12. “Stochastic Models, Estimation and Control Vol.III” by P S Maybeck
“Stochastic Models, Estimation and Control: Volume 3” Book Review: This book is a continuation of the Volumes 1 and 2 of the same series. The fundamentals of stochastic control and dynamic programming are introduced as a means of synthesizing optimal stochastic control laws. This is followed by LQG synthesis of controllers based upon linear system models, Gaussian noise models, and quadratic cost criteria for defining optimality. The book also develops practical nonlinear controllers using the concepts explained in Volumes 1 and 2. Numerous illustrations and examples are also provided. Prerequisite knowledge of advanced calculus, differential equations, basic vector and matrix analysis on an engineering level is required. This book is intended for engineers as well as students and researchers studying in engineering courses.


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