Introduction to Probability Models

Introduction to Probability Models

10th Edition - December 11, 2006

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  • Author: Sheldon Ross
  • eBook ISBN: 9780123756879

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Description

Introduction to Probability Models, Tenth Edition, provides an introduction to elementary probability theory and stochastic processes. There are two approaches to the study of probability theory. One is heuristic and nonrigorous, and attempts to develop in students an intuitive feel for the subject that enables him or her to think probabilistically. The other approach attempts a rigorous development of probability by using the tools of measure theory. The first approach is employed in this text. The book begins by introducing basic concepts of probability theory, such as the random variable, conditional probability, and conditional expectation. This is followed by discussions of stochastic processes, including Markov chains and Poison processes. The remaining chapters cover queuing, reliability theory, Brownian motion, and simulation. Many examples are worked out throughout the text, along with exercises to be solved by students. This book will be particularly useful to those interested in learning how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. Ideally, this text would be used in a one-year course in probability models, or a one-semester course in introductory probability theory or a course in elementary stochastic processes.

Key Features

New to this Edition:

  • 65% new chapter material including coverage of finite capacity queues, insurance risk models and Markov chains
  • Contains compulsory material for new Exam 3 of the Society of Actuaries containing several sections in the new exams
  • Updated data, and a list of commonly used notations and equations, a robust ancillary package, including a ISM, SSM, and test bank
  • Includes SPSS PASW Modeler and SAS JMP software packages which are widely used in the field

Hallmark features:

  • Superior writing style
  • Excellent exercises and examples covering the wide breadth of coverage of probability topics
  • Real-world applications in engineering, science, business and economics

Readership

Professionals and students in actuarial science, engineering, operations research, and other fields in applied probability.

Table of Contents


  • Preface

    1 Introduction to Probability Theory

    1.1 Introduction

    1.2 Sample Space and Events

    1.3 Probabilities Defined on Events

    1.4 Conditional Probabilities

    1.5 Independent Events

    1.6 Bayes’ Formula

    Exercises

    References

    2 Random Variables

    2.1 Random Variables

    2.2 Discrete Random Variables

    2.2.1 The Bernoulli Random Variable

    2.2.2 The Binomial Random Variable

    2.2.3 The Geometric Random Variable

    2.2.4 The Poisson Random Variable

    2.3 Continuous Random Variables

    2.3.1 The Uniform Random Variable

    2.3.2 Exponential Random Variables

    2.3.3 Gamma Random Variables

    2.3.4 Normal Random Variables

    2.4 Expectation of a Random Variable

    2.4.1 The Discrete Case

    2.4.2 The Continuous Case

    2.4.3 Expectation of a Function of a Random Variable

    2.5 Jointly Distributed Random Variables

    2.5.1 Joint Distribution Functions

    2.5.2 Independent Random Variables

    2.5.3 Covariance and Variance of Sums of Random Variables

    2.5.4 Joint Probability Distribution of Functions of Random Variables

    2.6 Moment Generating Functions

    2.6.1 The Joint Distribution of the Sample Mean and Sample Variance from a Normal Population

    2.7 The Distribution of the Number of Events that Occur

    2.8 Limit Theorems

    2.9 Stochastic Processes

    Exercises

    References

    3 Conditional Probability and Conditional Expectation

    3.1 Introduction

    3.2 The Discrete Case

    3.3 The Continuous Case

    3.4 Computing Expectations by Conditioning

    3.4.1 Computing Variances by Conditioning

    3.5 Computing Probabilities by Conditioning

    3.6 Some Applications

    3.6.1 A List Model

    3.6.2 A Random Graph

    3.6.3 Uniform Priors, Polyas Urn Model, and Bose–Einstein Statistics

    3.6.4 Mean Time for Patterns

    3.6.5 The k-Record Values of Discrete Random Variables

    3.6.6 Left Skip Free Random Walks

    3.7 An Identity for Compound Random Variables

    3.7.1 Poisson Compounding Distribution

    3.7.2 Binomial Compounding Distribution

    3.7.3 A Compounding Distribution Related to the Negative Binomial

    Exercises

    4 Markov Chains

    4.1 Introduction

    4.2 Chapman–Kolmogorov Equations

    4.3 Classification of States

    4.4 Limiting Probabilities

    4.5 Some Applications

    4.5.1 The Gamblers Ruin Problem

    4.5.2 A Model for Algorithmic Efficiency

    4.5.3 Using a Random Walk to Analyze a Probabilistic Algorithm for the Satisfiability Problem

    4.6 Mean Time Spent in Transient States

    4.7 Branching Processes

    4.8 Time Reversible Markov Chains

    4.9 Markov Chain Monte Carlo Methods

    4.10 Markov Decision Processes

    4.11 Hidden Markov Chains

    4.11.1 Predicting the States

    Exercises

    References

    5 The Exponential Distribution and the Poisson Process

    5.1 Introduction

    5.2 The Exponential Distribution

    5.2.1 Definition

    5.2.2 Properties of the Exponential Distribution

    5.2.3 Further Properties of the Exponential Distribution

    5.2.4 Convolutions of Exponential Random Variables

    5.3 The Poisson Process

    5.3.1 Counting Processes

    5.3.2 Definition of the Poisson Process

    5.3.3 Interarrival and Waiting Time Distributions

    5.3.4 Further Properties of Poisson Processes

    5.3.5 Conditional Distribution of the Arrival Times

    5.3.6 Estimating Software Reliability

    5.4 Generalizations of the Poisson Process

    5.4.1 Nonhomogeneous Poisson Process

    5.4.2 Compound Poisson Process

    5.4.3 Conditional or Mixed Poisson Processes

    Exercises

    References

    6 Continuous-Time Markov Chains

    6.1 Introduction

    6.2 Continuous-Time Markov Chains

    6.3 Birth and Death Processes

    6.4 The Transition Probability Function Pij(t)

    6.5 Limiting Probabilities

    6.6 Time Reversibility

    6.7 Uniformization

    6.8 Computing the Transition Probabilities

    Exercises

    References

    7 Renewal Theory and Its Applications

    7.1 Introduction

    7.2 Distribution of N(t)

    7.3 Limit Theorems and Their Applications

    7.4 Renewal Reward Processes

    7.5 Regenerative Processes

    7.5.1 Alternating Renewal Processes

    7.6 Semi-Markov Processes

    7.7 The Inspection Paradox

    7.8 Computing the Renewal Function

    7.9 Applications to Patterns

    7.9.1 Patterns of Discrete Random Variables

    7.9.2 The Expected Time to a Maximal Run of Distinct Values

    7.9.3 Increasing Runs of Continuous Random Variables

    7.10 The Insurance Ruin Problem

    Exercises

    References

    8 Queueing Theory

    8.1 Introduction

    8.2 Preliminaries

    8.2.1 Cost Equations

    8.2.2 Steady-State Probabilities

    8.3 Exponential Models

    8.3.1 A Single-Server Exponential Queueing System

    8.3.2 A Single-Server Exponential Queueing System Having Finite Capacity

    8.3.3 Birth and Death Queueing Models

    8.3.4 A Shoe Shine Shop

    8.3.5 A Queueing System with Bulk Service

    8.4 Network of Queues

    8.4.1 Open Systems

    8.4.2 Closed Systems

    8.5 The System M/G/1

    8.5.1 Preliminaries: Work and Another Cost Identity

    8.5.2 Application of Work to M/G/1

    8.5.3 Busy Periods

    8.6 Variations on the M/G/1

    8.6.1 The M/G/1 with Random-Sized Batch Arrivals

    8.6.2 Priority Queues

    8.6.3 An M/G/1 Optimization Example

    8.6.4 The M/G/1 Queue with Server Breakdown

    8.7 The Model G/M/1

    8.7.1 The G/M/1 Busy and Idle Periods

    8.8 A Finite Source Model

    8.9 Multiserver Queues

    8.9.1 Erlangs Loss System

    8.9.2 The M/M/k Queue

    8.9.3 The G/M/k Queue

    8.9.4 The M/G/k Queue

    Exercises

    References

    9 Reliability Theory

    9.1 Introduction

    9.2 Structure Functions

    9.2.1 Minimal Path and Minimal Cut Sets

    9.3 Reliability of Systems of Independent Components

    9.4 Bounds on the Reliability Function

    9.4.1 Method of Inclusion and Exclusion

    9.4.2 Second Method for Obtaining Bounds on r(p)

    9.5 System Life as a Function of Component Lives

    9.6 Expected System Lifetime

    9.6.1 An Upper Bound on the Expected Life of a Parallel System

    9.7 Systems with Repair

    9.7.1 A Series Model with Suspended Animation

    Exercises

    References

    10 Brownian Motion and Stationary Processes

    10.1 Brownian Motion

    10.2 Hitting Times, Maximum Variable, and the Gambler's Ruin Problem

    10.3 Variations on Brownian Motion

    10.3.1 Brownian Motion with Drift

    10.3.2 Geometric Brownian Motion

    10.4 Pricing Stock Options

    10.4.1 An Example in Options Pricing

    10.4.2 The Arbitrage Theorem

    10.4.3 The Black-Scholes Option Pricing Formula

    10.5 White Noise

    10.6 Gaussian Processes

    10.7 Stationary and Weakly Stationary Processes

    10.8 Harmonic Analysis of Weakly Stationary Processes

    Exercises

    References

    11 Simulation

    11.1 Introduction

    11.2 General Techniques for Simulating Continuous Random Variables

    11.2.1 The Inverse Transformation Method

    11.2.2 The Rejection Method

    11.2.3 The Hazard Rate Method

    11.3 Special Techniques for Simulating Continuous Random Variables

    11.3.1 The Normal Distribution

    11.3.2 The Gamma Distribution

    11.3.3 The Chi-Squared Distribution

    11.3.4 The Beta (n,m) Distribution

    11.3.5 The Exponential Distribution—The Von Neumann Algorithm

    11.4 Simulating from Discrete Distributions

    11.4.1 The Alias Method

    11.5 Stochastic Processes

    11.5.1 Simulating a Nonhomogeneous Poisson Process

    11.5.2 Simulating a Two-Dimensional Poisson Process

    11.6 Variance Reduction Techniques

    11.6.1 Use of Antithetic Variables

    11.6.2 Variance Reduction by Conditioning

    11.6.3 Control Variates

    11.6.4 Importance Sampling

    11.7 Determining the Number of Runs

    11.8 Generating from the Stationary Distribution of a Markov Chain

    11.8.1 Coupling from the Past

    11.8.2 Another Approach

    Exercises

    References

    Appendix: Solutions to Starred Exercises

    Index




Product details

  • No. of pages: 800
  • Language: English
  • Copyright: © Academic Press 2009
  • Published: December 11, 2006
  • Imprint: Academic Press
  • eBook ISBN: 9780123756879

About the Author

Sheldon Ross

Sheldon Ross
Dr. Sheldon M. Ross is a professor in the Department of Industrial and Systems Engineering at the University of Southern California. He received his PhD in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences. He is a Fellow of the Institute of Mathematical Statistics, a Fellow of INFORMS, and a recipient of the Humboldt US Senior Scientist Award.

Affiliations and Expertise

Professor, Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, USA

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