Fundamentals of Applied Probability and Random Processes
- 1st Edition - October 19, 2005
- Author: Oliver Ibe
- Language: English
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… Read more
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.
* 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
* 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
Third and fourth year engineering students, but can also be used at lower graduate levels.
Preface
Acknowledgments
Chapter 1 Basic Probability Concepts
1.1 Introduction
1.2 Sample Space and Events
1.3 Definitions of Probability
1.3.1 Axiomatic Definition
1.3.2 Relative-Frequency Definition
1.3.3 Classical Definition
1.4 Applications of Probability
1.4.1 Reliability Engineering
1.4.2 Quality Control
1.4.3 Channel Noise
1.4.4 System Simulation
1.5 Elementary Set Theory
1.5.1 Set Operations
1.5.2 Number of Subsets of a Set
1.5.3 Venn Diagram
1.5.4 Set Identities
1.5.5 Duality Principle
1.6 Properties of Probability
1.7 Conditional Probability
1.7.1 Total Probability and the Bayes’ Theorem
1.7.2 Tree Diagram
1.8 Independent Events
1.9 Combined Experiments
1.10 Basic Combinatorial Analysis
1.10.1 Permutations
1.10.2 Circular Arrangement
1.10.3 Applications of Permutations in Probability
1.10.4 Combinations
1.10.5 The Binomial Theorem
1.10.6 Stirling’s Formula
1.10.7 Applications of Combinations in Probability
1.11 Reliability Applications
1.12 Summary
1.13 Problems
1.14 References
Chapter 2 Random Variables
2.1 Introduction
2.2 Definition of a Random Variable
2.3 Events Defined by Random Variables
2.4 Distribution Functions
2.5 Discrete Random Variables
2.5.1 Obtaining the PMF from the CDF
2.6 Continuous Random Variables
2.7 Chapter Summary
2.8 Problems
Chapter 3 Moments of Random Variables
3.1 Introduction
3.2 Expectation
3.3 Expectation of Nonnegative Random Variables
3.4 Moments of Random Variables and the Variance
3.5 Conditional Expectations
3.6 The Chebyshev Inequality
3.7 The Markov Inequality
3.8 Chapter Summary
3.9 Problems
Chapter 4 Special Probability Distributions
4.1 Introduction
4.2 The Bernoulli Trial and Bernoulli Distribution
4.3 Binomial Distribution
4.4 Geometric Distribution
4.4.1 Modified Geometric Distribution
4.4.2 “Forgetfulness” Property of the Geometric Distribution
4.5 Pascal (or Negative Binomial) Distribution
4.6 Hypergeometric Distribution
4.7 Poisson Distribution
4.7.1 Poisson Approximation to the Binomial Distribution
4.8 Exponential Distribution
4.8.1 “Forgetfulness” Property of the Exponential Distribution
4.8.2 Relationship between the Exponential and Poisson Distributions
4.9 Erlang Distribution
4.10 Uniform Distribution
4.10.1 The Discrete Uniform Distribution
4.11 Normal Distribution
4.11.1 Normal Approximation to the Binomial Distribution
4.11.2 The Error Function
4.11.3 The Q-Function
4.12 The Hazard Function
4.13 Chapter Summary
4.14 Problems
Chapter 5 Multiple Random Variables
5.1 Introduction
5.2 Joint CDFs of Bivariate Random Variables
5.2.1 Properties of the Joint CDF
5.3 Discrete Random Variables
5.4 Continuous Random Variables
5.5 Determining Probabilities from a Joint CDF
5.6 Conditional Distributions
5.6.1 Conditional PMF for Discrete Random Variables
5.6.2 Conditional PDF for Continuous Random Variables
5.6.3 Conditional Means and Variances
5.6.4 Simple Rule for Independence
5.7 Covariance and Correlation Coefficient
5.8 Many Random Variables
5.9 Multinomial Distributions
5.10 Chapter Summary
5.11 Problems
Chapter 6 Functions of Random Variables
6.1 Introduction
6.2 Functions of One Random Variable
6.2.1 Linear Functions
6.2.2 Power Functions
6.3 Expectation of a Function of One Random Variable
6.3.1 Moments of a Linear Function
6.4 Sums of Independent Random Variables
6.4.1 Moments of the Sum of Random Variables
6.4.2 Sum of Discrete Random Variables
6.4.3 Sum of Independent Binomial Random Variables
6.4.4 Sum of Independent Poisson Random Variables
6.4.5 The Spare Parts Problem
6.5 Minimum of Two Independent Random Variables
6.6 Maximum of Two Independent Random Variables
6.7 Comparison of the Interconnection Models
6.8 Two Functions of Two Random Variables
6.8.1 Application of the Transformation Method
6.9 Laws of Large Numbers
6.10 The Central Limit Theorem
6.11 Order Statistics
6.12 Chapter Summary
6.13 Problems
Chapter 7 Transform Methods
7.1 Introduction
7.2 The Characteristic Function
7.2.1 Moment-Generating Property of the Characteristic Function
7.3 The s-Transform
7.3.1 Moment-Generating Property of the s-Transform
7.3.2 The s-Transforms of Some Well-Known PDFs
7.3.2.1 The s-Transform of the Exponential Distribution
7.3.2.2 The s-Transform of the Uniform Distribution
7.3.3 The s-Transform of the PDF of the Sum of Independent Random Variables
7.3.3.1 The s-Transform of the Erlang Distribution
7.4 The z-Transform
7.4.1 Moment-Generating Property of the z-Transform
7.4.2 The z-Transform of the Bernoulli Distribution
7.4.3 The z-Transform of the Binomial Distribution
7.4.4 The z-Transform of the Geometric Distribution
7.4.5 The z-Transform of the Poisson Distribution
7.4.6 The z-Transform of the PMF of Sum of Independent Random Variables
7.4.7 The z-Transform of the Pascal Distribution
7.5 Random Sum of Random Variables
7.6 Chapter Summary
7.7 Problems
Chapter 8 Introduction to Random Processes
8.1 Introduction
8.2 Classification of Random Processes
8.3 Characterizing a Random Process
8.3.1 Mean and Autocorrelation Function of a Random Process
8.3.2 The Autocovariance Function of a Random Process
8.4 Crosscorrelation and Crosscovariance Functions
8.4.1 Review of Some Trigonometric Identities
8.5 Stationary Random Processes
8.5.1 Strict-Sense Stationary Processes
8.5.2 Wide-Sense Stationary Processes
8.5.2.1 Properties of Autocorrelation Functions for WSS Processes
8.5.2.2 Autocorrelation Matrices for WSS Processes
8.5.2.3 Properties of Crosscorrelation Functions for WSS Processes
8.6 Ergodic Random Processes
8.7 Power Spectral Density
8.7.1 White Noise
8.8 Discrete-Time Random Processes
8.8.1 Mean, Autocorrelation Function and Autocovariance Function
8.8.2 Power Spectral Density
8.8.3 Sampling of Continuous-Time Processes
8.9 Chapter Summary
8.10 Problems
Chapter 9 Linear Systems with Random Inputs
9.1 Introduction
9.2 Overview of Linear Systems with Deterministic Inputs
9.2.Linear Systems with Continuous-Time Random Inputs
9.3 Linear Systems with Discrete-Time Random Inputs
9.4 Auto regressive Moving Average Process
9.4.1 Moving Average Process
9.4.2 Auto regressive Process
9.4.3 ARMA Process
9.5 Chapter Summary
9.6 Problems
Chapter 10 Some Models of Random Processes
10.1 Introduction
10.2 The Bernoulli Process
10.3 Random Walk
10.3.1 Gambler’s Ruin
10.4 The Gaussian Process
10.4.1 White Gaussian Noise Process
10.5 Poisson Process
10.5.1 Counting Processes
10.5.2 Independent Increment Processes
10.5.3 Stationary Increments
10.5.4 Definitions of a Poisson Process
10.5.5 Interarrival Times for the Poisson Process
10.5.6 Conditional and Joint PMFs for Poisson Processes
10.5.7 Compound Poisson Process
10.5.8 Combinations of Independent Poisson Processes
10.5.9 Competing Independent Poisson Processes
10.5.10 Subdivision of a Poisson Process and the Filtered Poisson Process 4
10.5.11 Random Incidence
10.5.12 Nonhomogeneous Poisson Process
10.6 Markov Processes
10.7 Discrete-time Markov Chains
10.7.1 State Transition Probability Matrix
10.7.2 The n-step State Transition Probability
10.7.3 State Transition Diagrams
10.7.4 Classification of States
10.7.5 Limiting-state Probabilities
10.7.6 Doubly Stochastic Matrix
10.8 Continuous-time Markov Chains
10.8.1 Birth and Death Processes
10.9 Gambler’s Ruin as a Markov Chain
10.10 Chapter Summary
10.11 Problems
Chapter 11 Introduction to Statistics
11.1 Introduction
11.2 Sampling Theory
11.2.1 The Sample Mean
11.2.2 The Sample Variance
11.2.3 Sampling Distributions
11.3 Estimation Theory
11.3.1 Point Estimate, Interval Estimate and Confidence Interval
11.3.2 Maximum Likelihood Estimation
11.3.3 Minimum Mean Squared Error Estimation
11.4 Hypothesis Testing
11.4.1 Hypothesis Test Procedure
11.4.2 Type I and Type II Errors
11.4.3 One-Tailed and Two-Tailed Tests
11.5 Curve Fitting and Linear Regression
11.6 Problems
Appendix 1: Table for the CDF of the Standard Normal Random Variable
Acknowledgments
Chapter 1 Basic Probability Concepts
1.1 Introduction
1.2 Sample Space and Events
1.3 Definitions of Probability
1.3.1 Axiomatic Definition
1.3.2 Relative-Frequency Definition
1.3.3 Classical Definition
1.4 Applications of Probability
1.4.1 Reliability Engineering
1.4.2 Quality Control
1.4.3 Channel Noise
1.4.4 System Simulation
1.5 Elementary Set Theory
1.5.1 Set Operations
1.5.2 Number of Subsets of a Set
1.5.3 Venn Diagram
1.5.4 Set Identities
1.5.5 Duality Principle
1.6 Properties of Probability
1.7 Conditional Probability
1.7.1 Total Probability and the Bayes’ Theorem
1.7.2 Tree Diagram
1.8 Independent Events
1.9 Combined Experiments
1.10 Basic Combinatorial Analysis
1.10.1 Permutations
1.10.2 Circular Arrangement
1.10.3 Applications of Permutations in Probability
1.10.4 Combinations
1.10.5 The Binomial Theorem
1.10.6 Stirling’s Formula
1.10.7 Applications of Combinations in Probability
1.11 Reliability Applications
1.12 Summary
1.13 Problems
1.14 References
Chapter 2 Random Variables
2.1 Introduction
2.2 Definition of a Random Variable
2.3 Events Defined by Random Variables
2.4 Distribution Functions
2.5 Discrete Random Variables
2.5.1 Obtaining the PMF from the CDF
2.6 Continuous Random Variables
2.7 Chapter Summary
2.8 Problems
Chapter 3 Moments of Random Variables
3.1 Introduction
3.2 Expectation
3.3 Expectation of Nonnegative Random Variables
3.4 Moments of Random Variables and the Variance
3.5 Conditional Expectations
3.6 The Chebyshev Inequality
3.7 The Markov Inequality
3.8 Chapter Summary
3.9 Problems
Chapter 4 Special Probability Distributions
4.1 Introduction
4.2 The Bernoulli Trial and Bernoulli Distribution
4.3 Binomial Distribution
4.4 Geometric Distribution
4.4.1 Modified Geometric Distribution
4.4.2 “Forgetfulness” Property of the Geometric Distribution
4.5 Pascal (or Negative Binomial) Distribution
4.6 Hypergeometric Distribution
4.7 Poisson Distribution
4.7.1 Poisson Approximation to the Binomial Distribution
4.8 Exponential Distribution
4.8.1 “Forgetfulness” Property of the Exponential Distribution
4.8.2 Relationship between the Exponential and Poisson Distributions
4.9 Erlang Distribution
4.10 Uniform Distribution
4.10.1 The Discrete Uniform Distribution
4.11 Normal Distribution
4.11.1 Normal Approximation to the Binomial Distribution
4.11.2 The Error Function
4.11.3 The Q-Function
4.12 The Hazard Function
4.13 Chapter Summary
4.14 Problems
Chapter 5 Multiple Random Variables
5.1 Introduction
5.2 Joint CDFs of Bivariate Random Variables
5.2.1 Properties of the Joint CDF
5.3 Discrete Random Variables
5.4 Continuous Random Variables
5.5 Determining Probabilities from a Joint CDF
5.6 Conditional Distributions
5.6.1 Conditional PMF for Discrete Random Variables
5.6.2 Conditional PDF for Continuous Random Variables
5.6.3 Conditional Means and Variances
5.6.4 Simple Rule for Independence
5.7 Covariance and Correlation Coefficient
5.8 Many Random Variables
5.9 Multinomial Distributions
5.10 Chapter Summary
5.11 Problems
Chapter 6 Functions of Random Variables
6.1 Introduction
6.2 Functions of One Random Variable
6.2.1 Linear Functions
6.2.2 Power Functions
6.3 Expectation of a Function of One Random Variable
6.3.1 Moments of a Linear Function
6.4 Sums of Independent Random Variables
6.4.1 Moments of the Sum of Random Variables
6.4.2 Sum of Discrete Random Variables
6.4.3 Sum of Independent Binomial Random Variables
6.4.4 Sum of Independent Poisson Random Variables
6.4.5 The Spare Parts Problem
6.5 Minimum of Two Independent Random Variables
6.6 Maximum of Two Independent Random Variables
6.7 Comparison of the Interconnection Models
6.8 Two Functions of Two Random Variables
6.8.1 Application of the Transformation Method
6.9 Laws of Large Numbers
6.10 The Central Limit Theorem
6.11 Order Statistics
6.12 Chapter Summary
6.13 Problems
Chapter 7 Transform Methods
7.1 Introduction
7.2 The Characteristic Function
7.2.1 Moment-Generating Property of the Characteristic Function
7.3 The s-Transform
7.3.1 Moment-Generating Property of the s-Transform
7.3.2 The s-Transforms of Some Well-Known PDFs
7.3.2.1 The s-Transform of the Exponential Distribution
7.3.2.2 The s-Transform of the Uniform Distribution
7.3.3 The s-Transform of the PDF of the Sum of Independent Random Variables
7.3.3.1 The s-Transform of the Erlang Distribution
7.4 The z-Transform
7.4.1 Moment-Generating Property of the z-Transform
7.4.2 The z-Transform of the Bernoulli Distribution
7.4.3 The z-Transform of the Binomial Distribution
7.4.4 The z-Transform of the Geometric Distribution
7.4.5 The z-Transform of the Poisson Distribution
7.4.6 The z-Transform of the PMF of Sum of Independent Random Variables
7.4.7 The z-Transform of the Pascal Distribution
7.5 Random Sum of Random Variables
7.6 Chapter Summary
7.7 Problems
Chapter 8 Introduction to Random Processes
8.1 Introduction
8.2 Classification of Random Processes
8.3 Characterizing a Random Process
8.3.1 Mean and Autocorrelation Function of a Random Process
8.3.2 The Autocovariance Function of a Random Process
8.4 Crosscorrelation and Crosscovariance Functions
8.4.1 Review of Some Trigonometric Identities
8.5 Stationary Random Processes
8.5.1 Strict-Sense Stationary Processes
8.5.2 Wide-Sense Stationary Processes
8.5.2.1 Properties of Autocorrelation Functions for WSS Processes
8.5.2.2 Autocorrelation Matrices for WSS Processes
8.5.2.3 Properties of Crosscorrelation Functions for WSS Processes
8.6 Ergodic Random Processes
8.7 Power Spectral Density
8.7.1 White Noise
8.8 Discrete-Time Random Processes
8.8.1 Mean, Autocorrelation Function and Autocovariance Function
8.8.2 Power Spectral Density
8.8.3 Sampling of Continuous-Time Processes
8.9 Chapter Summary
8.10 Problems
Chapter 9 Linear Systems with Random Inputs
9.1 Introduction
9.2 Overview of Linear Systems with Deterministic Inputs
9.2.Linear Systems with Continuous-Time Random Inputs
9.3 Linear Systems with Discrete-Time Random Inputs
9.4 Auto regressive Moving Average Process
9.4.1 Moving Average Process
9.4.2 Auto regressive Process
9.4.3 ARMA Process
9.5 Chapter Summary
9.6 Problems
Chapter 10 Some Models of Random Processes
10.1 Introduction
10.2 The Bernoulli Process
10.3 Random Walk
10.3.1 Gambler’s Ruin
10.4 The Gaussian Process
10.4.1 White Gaussian Noise Process
10.5 Poisson Process
10.5.1 Counting Processes
10.5.2 Independent Increment Processes
10.5.3 Stationary Increments
10.5.4 Definitions of a Poisson Process
10.5.5 Interarrival Times for the Poisson Process
10.5.6 Conditional and Joint PMFs for Poisson Processes
10.5.7 Compound Poisson Process
10.5.8 Combinations of Independent Poisson Processes
10.5.9 Competing Independent Poisson Processes
10.5.10 Subdivision of a Poisson Process and the Filtered Poisson Process 4
10.5.11 Random Incidence
10.5.12 Nonhomogeneous Poisson Process
10.6 Markov Processes
10.7 Discrete-time Markov Chains
10.7.1 State Transition Probability Matrix
10.7.2 The n-step State Transition Probability
10.7.3 State Transition Diagrams
10.7.4 Classification of States
10.7.5 Limiting-state Probabilities
10.7.6 Doubly Stochastic Matrix
10.8 Continuous-time Markov Chains
10.8.1 Birth and Death Processes
10.9 Gambler’s Ruin as a Markov Chain
10.10 Chapter Summary
10.11 Problems
Chapter 11 Introduction to Statistics
11.1 Introduction
11.2 Sampling Theory
11.2.1 The Sample Mean
11.2.2 The Sample Variance
11.2.3 Sampling Distributions
11.3 Estimation Theory
11.3.1 Point Estimate, Interval Estimate and Confidence Interval
11.3.2 Maximum Likelihood Estimation
11.3.3 Minimum Mean Squared Error Estimation
11.4 Hypothesis Testing
11.4.1 Hypothesis Test Procedure
11.4.2 Type I and Type II Errors
11.4.3 One-Tailed and Two-Tailed Tests
11.5 Curve Fitting and Linear Regression
11.6 Problems
Appendix 1: Table for the CDF of the Standard Normal Random Variable
- Edition: 1
- Published: October 19, 2005
- Language: English
OI
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