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Fundamentals of Applied Probability and Random Processes - 1st Edition - ISBN: 9780120885084, 9780080492704

Fundamentals of Applied Probability and Random Processes

1st Edition

Authors: Oliver Ibe Oliver Ibe
eBook ISBN: 9780080492704
Imprint: Academic Press
Published Date: 19th October 2005
Page Count: 456
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This book is based on the premise that engineers use probability as a modeling tool, and that probability can be applied to the solution of engineering problems. Engineers and students studying probability and random processes also need to analyze data, and thus need some knowledge of statistics. This book is designed to provide students with a thorough grounding in probability and stochastic processes, demonstrate their applicability to real-world problems, and introduce the basics of statistics. The book's clear writing style and homework problems make it ideal for the classroom or for self-study.

Key Features

  • Good and solid introduction to probability theory and stochastic processes
  • Logically organized; writing is presented in a clear manner
  • Choice of topics is comprehensive within the area of probability
  • Ample homework problems are organized into chapter sections


Juniors and Seniors, but can also be used at lower graduate levels. Particularily welcome at engineering schools.

Table of Contents

Preface <BR id=""CRLF"">Acknowledgments<BR id=""CRLF"">Chapter 1 Basic Probability Concepts <BR id=""CRLF"">1.1 Introduction <BR id=""CRLF"">1.2 Sample Space and Events <BR id=""CRLF"">1.3 Definitions of Probability <BR id=""CRLF""> 1.3.1 Axiomatic Definition<BR id=""CRLF""> 1.3.2 Relative-Frequency Definition <BR id=""CRLF""> 1.3.3 Classical Definition <BR id=""CRLF"">1.4 Applications of Probability <BR id=""CRLF""> 1.4.1 Reliability Engineering<BR id=""CRLF""> 1.4.2 Quality Control <BR id=""CRLF""> 1.4.3 Channel Noise <BR id=""CRLF""> 1.4.4 System Simulation <BR id=""CRLF"">1.5 Elementary Set Theory <BR id=""CRLF""> 1.5.1 Set Operations<BR id=""CRLF""> 1.5.2 Number of Subsets of a Set <BR id=""CRLF""> 1.5.3 Venn Diagram <BR id=""CRLF""> 1.5.4 Set Identities <BR id=""CRLF""> 1.5.5 Duality Principle <BR id=""CRLF"">1.6 Properties of Probability <BR id=""CRLF"">1.7 Conditional Probability<BR id=""CRLF""> 1.7.1 Total Probability and the Bayes’ Theorem <BR id=""CRLF""> 1.7.2 Tree Diagram <BR id=""CRLF"">1.8 Independent Events<BR id=""CRLF"">1.9 Combined Experiments <BR id=""CRLF"">1.10 Basic Combinatorial Analysis <BR id=""CRLF""> 1.10.1 Permutations <BR id=""CRLF""> 1.10.2 Circular Arrangement <BR id=""CRLF""> 1.10.3 Applications of Permutations in Probability <BR id=""CRLF""> 1.10.4 Combinations <BR id=""CRLF""> 1.10.5 The Binomial Theorem <BR id=""CRLF""> 1.10.6 Stirling’s Formula <BR id=""CRLF""> 1.10.7 Applications of Combinations in Probability <BR id=""CRLF"">1.11 Reliability Applications <BR id=""CRLF"">1.12 Summary <BR id=""CRLF"">1.13 Problems <BR id=""CRLF"">1.14 References<BR id=""CRLF"">Chapter 2 Random Variables <BR id=""CRLF"">2.1 Introduction <BR id=""CRLF"">2.2 Definition of a Random Variable <BR id=""CRLF"">2.3 Events Defined by Random Variables<BR id=""CRLF"">2.4 Distribution Functions <BR id=""CRLF"">2.5 Discrete Random Variables <BR id=""CRLF""> 2.5.1 Obtaining the PMF from the CDF <BR id=""CRLF"">2.6 Continuous Random Variables <BR id=""CRLF"">2.7 Chapter Summary <BR id=""CRLF"">2.8 Problems<BR id=""CRLF"">Chapter 3 Moments of Random Variables <BR id=""CRLF"">3.1 Introduction <BR id=""CRLF"">3.2 Expectation <BR id=""CRLF"">3.3 Expectation of Nonnegative Random Variables <BR id=""CRLF"">3.4 Moments of Random Variables and the Variance <BR id=""CRLF"">3.5 Conditional Expectations <BR id=""CRLF"">3.6 The Chebyshev Inequality<BR id=""CRLF"">3.7 The Markov Inequality <BR id=""CRLF"">3.8 Chapter Summary <BR id=""CRLF"">3.9 Problems<BR id=""CRLF"">Chapter 4 Special Probability Distributions <BR id=""CRLF"">4.1 Introduction<BR id=""CRLF"">4.2 The Bernoulli Trial and Bernoulli Distribution <BR id=""CRLF"">4.3 Binomial Distribution<BR id=""CRLF"">4.4 Geometric Distribution<BR id=""CRLF""> 4.4.1 Modified Geometric Distribution <BR id=""CRLF""> 4.4.2 “Forgetfulness” Property of the Geometric Distribution <BR id=""CRLF"">4.5 Pascal (or Negative Binomial) Distribution <BR id=""CRLF"">4.6 Hypergeometric Distribution <BR id=""CRLF"">4.7 Poisson Distribution <BR id=""CRLF""> 4.7.1 Poisson Approximation to the Binomial Distribution <BR id=""CRLF"">4.8 Exponential Distribution <BR id=""CRLF""> 4.8.1 “Forgetfulness” Property of the Exponential Distribution <BR id=""CRLF""> 4.8.2 Relationship between the Exponential and Poisson Distributions <BR id=""CRLF"">4.9 Erlang Distribution<BR id=""CRLF"">4.10 Uniform Distribution <BR id=""CRLF""> 4.10.1 The Discrete Uniform Distribution <BR id=""CRLF"">4.11 Normal Distribution <BR id=""CRLF""> 4.11.1 Normal Approximation to the Binomial Distribution <BR id=""CRLF""> 4.11.2 The Error Function <BR id=""CRLF""> 4.11.3 The Q-Function <BR id=""CRLF"">4.12 The Hazard Function <BR id=""CRLF"">4.13 Chapter Summary <BR id=""CRLF"">4.14 Problems<BR id=""CRLF"">Chapter 5 Multiple Random Variables <BR id=""CRLF"">5.1 Introduction <BR id=""CRLF"">5.2 Joint CDFs of Bivariate Random Variables <BR id=""CRLF""> 5.2.1 Properties of the Joint CDF <BR id=""CRLF"">5.3 Discrete Random Variables<BR id=""CRLF"">5.4 Continuous Random Variables <BR id=""CRLF"">5.5 Determining Probabilities from a Joint CDF <BR id=""CRLF"">5.6 Conditional Distributions <BR id=""CRLF""> 5.6.1 Conditional PMF for Discrete Random Variables <BR id=""CRLF""> 5.6.2 Conditional PDF for Continuous Random Variables <BR id=""CRLF""> 5.6.3 Conditional Means and Variances<BR id=""CRLF""> 5.6.4 Simple Rule for Independence <BR id=""CRLF"">5.7 Covariance and Correlation Coefficient <BR id=""CRLF"">5.8 Many Random Variables <BR id=""CRLF"">5.9 Multinomial Distributions <BR id=""CRLF"">5.10 Chapter Summary <BR id=""CRLF"">5.11 Problems <BR id=""CRLF"">Chapter 6 Functions of Random Variables <BR id=""CRLF"">6.1 Introduction <BR id=""CRLF"">6.2 Functions of One Random Variable<BR id=""CRLF""> 6.2.1 Linear Functions<BR id=""CRLF""> 6.2.2 Power Functions <BR id=""CRLF"">6.3 Expectation of a Function of One Random Variable <BR id=""CRLF""> 6.3.1 Moments of a Linear Function <BR id=""CRLF"">6.4 Sums of Independent Random Variables <BR id=""CRLF""> 6.4.1 Moments of the Sum of Random Variables <BR id=""CRLF""> 6.4.2 Sum of Discrete Random Variables <BR id=""CRLF""> 6.4.3 Sum of Independent Binomial Random Variables <BR id=""CRLF""> 6.4.4 Sum of Independent Poisson Random Variables <BR id=""CRLF""> 6.4.5 The Spare Parts Problem <BR id=""CRLF"">6.5 Minimum of Two Independent Random Variables<BR id=""CRLF"">6.6 Maximum of Two Independent Random Variables<BR id=""CRLF"">6.7 Comparison of the Interconnection Models <BR id=""CRLF"">6.8 Two Functions of Two Random Variables <BR id=""CRLF""> 6.8.1 Application of the Transformation Method<BR id=""CRLF"">6.9 Laws of Large Numbers <BR id=""CRLF"">6.10 The Central Limit Theorem <BR id=""CRLF"">6.11 Order Statistics <BR id=""CRLF"">6.12 Chapter Summary <BR id=""CRLF"">6.13 Problems <BR id=""CRLF"">Chapter 7 Transform Methods <BR id=""CRLF"">7.1 Introduction <BR id=""CRLF"">7.2 The Characteristic Function <BR id=""CRLF""> 7.2.1 Moment-Generating Property of the Characteristic Function <BR id=""CRLF"">7.3 The s-Transform<BR id=""CRLF""> 7.3.1 Moment-Generating Property of the s-Transform<BR id=""CRLF""> 7.3.2 The s-Transforms of Some Well-Known PDFs <BR id=""CRLF""> The s-Transform of the Exponential Distribution <BR id=""CRLF""> The s-Transform of the Uniform Distribution <BR id=""CRLF""> 7.3.3 The s-Transform of the PDF of the Sum of Independent Random Variables <BR id=""CRLF""> The s-Transform of the Erlang Distribution<BR id=""CRLF"">7.4 The z-Transform<BR id=""CRLF""> 7.4.1 Moment-Generating Property of the z-Transform <BR id=""CRLF""> 7.4.2 The z-Transform of the Bernoulli Distribution <BR id=""CRLF""> 7.4.3 The z-Transform of the Binomial Distribution <BR id=""CRLF""> 7.4.4 The z-Transform of the Geometric Distribution <BR id=""CRLF""> 7.4.5 The z-Transform of the Poisson Distribution <BR id=""CRLF""> 7.4.6 The z-Transform of the PMF of Sum of Independent Random Variables<BR id=""CRLF""> 7.4.7 The z-Transform of the Pascal Distribution <BR id=""CRLF"">7.5 Random Sum of Random Variables <BR id=""CRLF"">7.6 Chapter Summary <BR id=""CRLF"">7.7 Problems<BR id=""CRLF"">Chapter 8 Introduction to Random Processes <BR id=""CRLF"">8.1 Introduction<BR id=""CRLF"">8.2 Classification of Random Processes <BR id=""CRLF"">8.3 Characterizing a Random Process <BR id=""CRLF""> 8.3.1 Mean and Autocorrelation Function of a Random Process<BR id=""CRLF""> 8.3.2 The Autocovariance Function of a Random Process <BR id=""CRLF"">8.4 Crosscorrelation and Crosscovariance Functions<BR id=""CRLF""> 8.4.1 Review of Some Trigonometric Identities<BR id=""CRLF"">8.5 Stationary Random Processes<BR id=""CRLF""> 8.5.1 Strict-Sense Stationary Processes <BR id=""CRLF""> 8.5.2 Wide-Sense Stationary Processes<BR id=""CRLF""> Properties of Autocorrelation Functions for WSS Processes <BR id=""CRLF""> Autocorrelation Matrices for WSS Processes<BR id=""CRLF""> Properties of Crosscorrelation Functions for WSS Processes<BR id=""CRLF"">8.6 Ergodic Random Processes<BR id=""CRLF"">8.7 Power Spectral Density<BR id=""CRLF""> 8.7.1 White Noise <BR id=""CRLF"">8.8 Discrete-Time Random Processes<BR id=""CRLF""> 8.8.1 Mean, Autocorrelation Function and Autocovariance Function <BR id=""CRLF""> 8.8.2 Power Spectral Density <BR id=""CRLF""> 8.8.3 Sampling of Continuous-Time Processes <BR id=""CRLF"">8.9 Chapter Summary<BR id=""CRLF"">8.10 Problems<BR id=""CRLF"">Chapter 9 Linear Systems with Random Inputs<BR id=""CRLF"">9.1 Introduction<BR id=""CRLF"">9.2 Overview of Linear Systems with Deterministic Inputs <BR id=""CRLF"">9.2.Linear Systems with Continuous-Time Random Inputs <BR id=""CRLF"">9.3 Linear Systems with Discrete-Time Random Inputs <BR id=""CRLF"">9.4 Auto regressive Moving Average Process <BR id=""CRLF""> 9.4.1 Moving Average Process <BR id=""CRLF""> 9.4.2 Auto regressive Process <BR id=""CRLF""> 9.4.3 ARMA Process <BR id=""CRLF"">9.5 Chapter Summary <BR id=""CRLF"">9.6 Problems<BR id=""CRLF"">Chapter 10 Some Models of Random Processes <BR id=""CRLF"">10.1 Introduction<BR id=""CRLF"">10.2 The Bernoulli Process <BR id=""CRLF"">10.3 Random Walk <BR id=""CRLF""> 10.3.1 Gambler’s Ruin <BR id=""CRLF"">10.4 The Gaussian Process <BR id=""CRLF""> 10.4.1 White Gaussian Noise Process <BR id=""CRLF"">10.5 Poisson Process <BR id=""CRLF""> 10.5.1 Counting Processes <BR id=""CRLF""> 10.5.2 Independent Increment Processes<BR id=""CRLF""> 10.5.3 Stationary Increments <BR id=""CRLF""> 10.5.4 Definitions of a Poisson Process <BR id=""CRLF""> 10.5.5 Interarrival Times for the Poisson Process <BR id=""CRLF""> 10.5.6 Conditional and Joint PMFs for Poisson Processes <BR id=""CRLF""> 10.5.7 Compound Poisson Process <BR id=""CRLF""> 10.5.8 Combinations of Independent Poisson Processes <BR id=""CRLF""> 10.5.9 Competing Independent Poisson Processes <BR id=""CRLF""> 10.5.10 Subdivision of a Poisson Process and the Filtered Poisson Process 4<BR id=""CRLF""> 10.5.11 Random Incidence <BR id=""CRLF""> 10.5.12 Nonhomogeneous Poisson Process <BR id=""CRLF"">10.6 Markov Processes <BR id=""CRLF"">10.7 Discrete-time Markov Chains <BR id=""CRLF""> 10.7.1 State Transition Probability Matrix <BR id=""CRLF""> 10.7.2 The n-step State Transition Probability <BR id=""CRLF""> 10.7.3 State Transition Diagrams <BR id=""CRLF""> 10.7.4 Classification of States<BR id=""CRLF""> 10.7.5 Limiting-state Probabilities <BR id=""CRLF""> 10.7.6 Doubly Stochastic Matrix<BR id=""CRLF"">10.8 Continuous-time Markov Chains<BR id=""CRLF""> 10.8.1 Birth and Death Processes <BR id=""CRLF"">10.9 Gambler’s Ruin as a Markov Chain <BR id=""CRLF"">10.10 Chapter Summary <BR id=""CRLF"">10.11 Problems <BR id=""CRLF"">Chapter 11 Introduction to Statistics <BR id=""CRLF"">11.1 Introduction <BR id=""CRLF"">11.2 Sampling Theory <BR id=""CRLF""> 11.2.1 The Sample Mean <BR id=""CRLF""> 11.2.2 The Sample Variance<BR id=""CRLF""> 11.2.3 Sampling Distributions <BR id=""CRLF"">11.3 Estimation Theory <BR id=""CRLF""> 11.3.1 Point Estimate, Interval Estimate and Confidence Interval <BR id=""CRLF""> 11.3.2 Maximum Likelihood Estimation <BR id=""CRLF""> 11.3.3 Minimum Mean Squared Error Estimation <BR id=""CRLF"">11.4 Hypothesis Testing <BR id=""CRLF""> 11.4.1 Hypothesis Test Procedure<BR id=""CRLF""> 11.4.2 Type I and Type II Errors<BR id=""CRLF""> 11.4.3 One-Tailed and Two-Tailed Tests <BR id=""CRLF"">11.5 Curve Fitting and Linear Regression <BR id=""CRLF"">11.6 Problems <BR id=""CRLF"">Appendix 1: Table for the CDF of the Standard Normal Random Variable


No. of pages:
© Academic Press 2006
19th October 2005
Academic Press
eBook ISBN:

About the Authors

Oliver Ibe

Dr Ibe has been teaching at U Mass since 2003. He also has more than 20 years of experience in the corporate world, most recently as Chief Technology Officer at Sineria Networks and Director of Network Architecture for Spike Broadband Corp.

Affiliations and Expertise

University of Massachusetts, Lowell, USA

Oliver Ibe

Dr Ibe has been teaching at U Mass since 2003. He also has more than 20 years of experience in the corporate world, most recently as Chief Technology Officer at Sineria Networks and Director of Network Architecture for Spike Broadband Corp.

Affiliations and Expertise

University of Massachusetts, Lowell, USA


"Each chapter is broken down into small subunits, making this a useful reference book as well as a textbook. The material is presented clearly, and solved problems are included in the text." --MAA Reviews

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