Probability and Stochastic Modeling

Probability and Stochastic Modeling : The Mathematics of Insurance

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A First Course in Probability with an Emphasis on Stochastic Modeling
Probability and Stochastic Modeling not only covers all the topics found in a traditional introductory probability course, but also emphasizes stochastic modeling, including Markov chains, birth-death processes, and reliability models. Unlike most undergraduate-level probability texts, the book also focuses on increasingly important areas, such as martingales, classification of dependency structures, and risk evaluation. Numerous examples, exercises, and models using real-world data demonstrate the practical possibilities and restrictions of different approaches and help students grasp general concepts and theoretical results.

The text is suitable for majors in mathematics and statistics as well as majors in computer science, economics, finance, and physics. The author offers two explicit options to teaching the material, which is reflected in "routes" designated by special "roadside" markers. The first route contains basic, self-contained material for a one-semester course. The second provides a more complete exposition for a two-semester course or self-study.
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Product details

  • Mixed media product | 656 pages
  • 177.8 x 251.5 x 40.6mm | 1,156.67g
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 13 Tables, black and white; 110 Illustrations, black and white
  • 1584885866
  • 9781584885863

Table of contents

Basic Notions
Sample Space and Events
Counting Techniques

Independence and Conditional Probability
The Borel-Cantelli Theorem

Discrete Random Variables
Random Variables and Vectors
Expected Value
Variance and Other Moments. Inequalities for Deviations
Some Basic Distributions
Convergence of Random Variables. The Law of Large Numbers
Conditional Expectation

Generating Functions. Branching Processes. Random Walk Revisited
Branching Processes
Generating Functions
Branching Processes Revisited
More on Random Walk

Markov Chains
Definitions and Examples. Probability Distributions of Markov Chains
The First Step Analysis. Passage Times
Variables Defined on a Markov Chain
Ergodicity and Stationary Distributions
A Classification of States and Ergodicity

Continuous Random Variables
Continuous Distributions
Some Basic Distributions
Continuous Multivariate Distributions
Sums of Independent Random Variables
Conditional Distributions and Expectations

Distributions in the General Case. Simulation
Distribution Functions
Expected Values
On Convergence in Distribution and Probability

Moment Generating Functions
Definitions and Properties
Some Examples of Applications
Exponential or Bernstein-Chernoff's Bounds

The Central Limit Theorem for Independent Random Variables
The Central Limit Theorem (CLT) for Independent and Identically Distributed Random Variables
The CLT for Independent Variables in the General Case

Covariance Analysis. The Multivariate Normal Distribution. The Multivariate Central Limit Theorem
Covariance and Correlation
Covariance Matrices and Some Applications
The Multivariate Normal Distribution

Maxima and Minima of Random Variables. Elements of Reliability Theory. Hazard Rate and Survival Probabilities
Maxima and Minima of Random Variables. Reliability Characteristics
Limit Theorems for Maxima and Minima
Hazard Rate. Survival Probabilities

Stochastic Processes: Preliminaries
A General Definition
Processes with Independent Increments
Brownian Motion
Markov Processes
A Representation and Simulation of Markov Processes in Discrete Time

Counting and Queuing Processes. Birth and Death Processes: A General Scheme
Poisson Processes
Birth and Death Processes

Elements of Renewal Theory
Limit Theorems
Some Proofs

Martingales in Discrete Time
Definitions and Properties
Optional Time and Some Applications
Martingales and a Financial Market Model
Limit Theorems for Martingales

Brownian Motion and Martingales in Continuous Time
Brownian Motion and Its Generalizations
Martingales in Continuous Time

More on Dependency Structures
Arrangement Structures and the Corresponding Dependencies
Measures of Dependency
Limit Theorems for Dependent Random Variables
Symmetric Distributions. De Finetti's Theorem

Comparison of Random Variables. Risk Evaluation
Some Particular Criteria
Expected Utility
Generalizations of the EUM Criterion



Answers to Exercises


Exercises appear at the end of each chapter.
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Review quote

"This book covers practically all essential topics from modern probability and stochastic processes, which in one or another way are included in various university programs. So, is there something making this book different or better than many others available in the market? The answer is yes! The author presents the material systematically and rigorously. Of primary importance, however, is the emphasis on the modeling aspects. The large number of well-chosen and carefully described practical cases is a tremendous advantage of the book. ... strongly recommended as a basic source for a variety of courses."
-G. Schroeder, Zentralblatt MATH 1268

"The author's mode of presentation is well chosen. New concepts are introduced with illuminating discussion and illustrations. ... The book is physically nicely presented. ... this text has something new to offer, mainly for business-oriented students, and I recommend that instructors give it serious consideration when choosing a text for their courses.
-Anthony G. Pakes, Mathematical Reviews, September 2013

"This is a superbly written text on probability and stochastic processes for students who have had two semesters of calculus and an introductory course in linear algebra. This includes upper division students in science and engineering including statistics and mathematics, as well as students in fields such as economics and finance. In addition, it will be a wonderful book for self study for many others. Important and well-chosen examples illustrate the theory throughout, and a large body of exercises supplements the text. It gives a lucid presentation of basic probability theory, including Markov chains and martingales. A special feature of this book is a marvelous exposition of many interesting aspects of financial mathematics that are generally considered rather intricate and inaccessible at this level. This book carries the imprint of a distinguished mathematician and teacher with expertise in probability theory and many of its special applications to mathematical economics and finance. It is an outstanding addition to the field requiring only a modest background in mathematics."
-Rabi Bhattacharya, Department of Mathematics, University of Arizona, Tucson, USA

"Written in a lively and stimulating manner, the book makes a very good impression. The author, having extensive teaching experience and an undoubted literary talent, has managed to create an original introduction to modern probability theory. The successful combination of a variety of examples, exercises and applications with deep and nontrivial ideas makes the book interesting not only for beginning students, but also for professionals working with probabilistic problems. I believe that the book can serve as an ideal textbook for anyone interested in probability theory and its applications. The book will take a worthy place in the literature on probabilistic issues."
-Youri Davydov, Laboratoire Paul Painleve, Universite des Sciences et Technologies de Lille, France

"The author has produced a comprehensive introduction to probability theory and stochastic processes, including martingales and Brownian motion. The text is suitable for students with a standard background in calculus and linear algebra. The approach is rigorous without being pedantic, and the text is liberally sprinkled with examples. Throughout, there is a welcome emphasis on stochastic modeling. Of note is the fairly early introduction and use of conditional expectations.
The main text is complemented by a large collection of exercises with a wide range of difficulty. The book is in fact two-in-one, as a series of `roadside markers' guides the reader through two possible courses of study, one consisting of material suitable for a one-semester course, and the other a more in-depth journey suitable for a two-semester course. This book is a welcome and attractive addition to the list of textbooks available for an upper division probability course and would even be suitable for a graduate-level introduction to non-measure-theoretic probability and stochastic processes."
-Patrick J. Fitzsimmons, Department of Mathematics, University of California-San Diego, La Jolla, USA
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About Vladimir I. Rotar

Vladimir I. Rotar is a professor in the Department of Mathematics and Statistics at San Diego State University. Dr. Rotar has authored four books and more than 100 scientific papers on probability theory and its applications in leading mathematical journals.
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