 ## Probability Theory and Stochastic Processes

#### Price: 850.00

This book provides an introduction into the mathematical concepts and tools necessary for understanding the theory of probability and the dynamics of stochastic processes central to a number of application areas in engineering sciences, biology medicine and finance. The material covered in the book, however, is particularly suited to an undergraduate programme in electronics and communications engineering, for it focuses on probability and the random variable, on random processes, linear systems and probabilistic tools for modelling of noise, which are of direct relevance to this branch of engineering. Each topic is introduced with the fundamental concepts and underlying theories in a concise manner, and is then followed up with several worked-out examples for developing problem-solving skills in the learner. Many of the problems have been drawn from previous years'' examination papers to give students an exposure to the variety and kinds typically encountered in exam situations; the focus of the selection is to train them in the use of explicit probability distributions for solving engineering and physics problems.

The contents are mapped to JNTU ECE syllabus, and many of the solved examples and exercises are drawn from previous JNTU question papers, making it a good match for JNTU market. However, since the coverage of topics is typical of any undergraduate programme on the subject, the book can be promoted everywhere in the country.

Y Mallikarjuna Reddy, Principal, Vasireddy Venkitadri Institute of Technology, Nambur, Guntur, is a professor in the department of Electronics and Communications Engineering. He has more than 22 years of teaching experience. He obtained his MTech degree from JNTU Kakinada in 1990 and his PhD in 2009 from Osmania University, Hyderabad. He is a member of I(E) and ISTE, India. He has published research papers in journals of international repute and is currently engaged in research in the areas of radar signal and image processing.

Preface
1 Introduction to Probability
1.1 Introduction
1.2 Set Theory
1.2.1 Terms of Set Theory
1.2.2 Set Operations
1.2.3 Laws of Sets
1.2.4 Sample Spaces
1.2.5 Events
1.3 The Relative Frequency and Axioms of Probability
1.3.1 Probability Introduced through Relative Frequency
1.3.2 Probability Introduced through Axioms
1.3.3 Classical Definition of Probability
1.4 Mathematical Model of Experiments
1.4.1 Examples of Experiments
1.5 Joint and Conditional Probability
1.5.1 Joint Probability
1.5.2 Conditional Probability
1.5.3 Properties of Conditional Probability
1.6 Total Probability Theorem
1.7 Bayes’ Theorem
1.8 Independent Events
1.8.1 Multiplication Theorem of Probability
1.8.2 Properties of Independent Events
1.9 Combined Sample Space
1.9.1 Independent Experiments
1.9.2 Permutations and Combinations
1.10 Bernoulli Trials
More Solved Examples
Questions
Problems
Multiple-Choice Questions

2 The Random Variable
2.1 Introduction
2.1.1 Random Variable
2.1.2 Classifications of Random Variables
2.2 Probability Distribution Function
2.2.1 Expression for Distribution Function
2.3 Probability density function
2.3.1 Expression for Density Function
2.3.2 Properties of Probability Distribution Functions
2.3.3 Properties of Probability Density Functions
2.3.4 Probability Mass Function
2.4 Examples of Distribution and Density Functions
2.4.1 The Gaussian Density Function
2.4.2 Uniform Density Function
2.4.3 Exponential Probability Density Function
2.4.4 Rayleigh Probability Density Function
2.4.5 Binomial Probability Density Function
2.4.6 Poisson Probability Density Function
2.5 Conditional Distribution Function
2.5.1 Properties of Conditional Distribution Function
2.6 Conditional Density Function
2.6.1 Properties of Conditional Density Functions
2.7 Distribution Function for a Conditional Event
More Solved Examples
Questions
Problems
Multiple-Choice Questions

3 Operations on One Random Variable
3.1 Introduction
3.2 Mathematical expectation
3.2.1 Expected Value of a Random Variable
3.2.2 Expected Value of a Function of a Random Variable
3.2.3 Conditional Expectation of a Random Variable
3.3 Properties of Expectation
3.4 Moments
3.5 Variance
3.5.1 Physical Significance of Variance and Standard Deviation
3.5.2 Skew and Coefficient of Skewness
3.5.3 Properties of Variance
3.5.4 Relationship between Central Moments and Moments about Origin
3.6 Functions for Moments
3.6.1 Characteristic Function
3.6.2 Properties of Characteristic Function
3.6.3 Moment Generating Function
3.6.4 Properties of Moment Generating Function
3.7 Inequalities
3.7.1 Chebychev’s Inequality
3.7.2 Markov Inequality
3.7.3 Chernoff’s Inequality & Bound
3.8 Transformations of a Random Variable
3.8.1 Monotonic Transformation of a Continuous Random Variable
3.8.2 Non-Monotonic Transformation of a Continuous Random Variable
3.8.3 Transformation of a Discrete Random Variable
More Solved Examples
Questions
Problems
Multiple-Choice Questions

4 Multiple Random Variables
4.1 Introduction
4.2 Joint probability Distribution Function
4.2.1 Properties of Joint Distribution Function
4.3 Joint Probability Density Function
4.3.1 Properties of Joint Density Function
4.4 Conditional Distribution and Density Functions
4.4.1 Point Conditioning
4.4.2 Internal Conditioning
4.5 Statistical Independence of Random Variables
4.6 Sum of Random Variables
4.6.1 Two Random Variables
4.6.2 Multiple Random Variables 4.7 Central Limit Theorem
4.8 Probability Mass Function
More Solved Examples Questions
Problems
Multiple-Choice Questions 3

5 Operations on Multiple Random Variables
5.1 Introduction
5.2 Function of Joint Random Variables
5.3 Joint Moments
5.3.1 Joint Moments about the Origin
5.3.2 Correlation
5.3.3 Properties of Correlation
5.3.4 Joint Central Moments
5.3.5 Covariance
5.3.6 Correlation Coefficient
5.3.7 Properties of Covariance
5.4 Joint Characteristic Function
5.4.1 Properties of Joint Characteristic Function
5.5 Joint Moment Generating Function
5.5.1 Properties of Joint Moment Generating Function
5.6 Gaussian Random Variables
5.6.1 Two Random Variables
5.6.2 N Random Variables
5.6.3 Properties of Gaussian Random Variables
5.7 Transformation of Random Variables
5.8 Linear Transformation of Gaussian Random Variables
5.9 Conditional Gaussian Density Functions
More Solved Examples
Questions
Problems
Multiple-Choice Questions

6 Random Processes
6.1 Introduction
6.2 Random Process
6.3 Classification of Random Processes
6.3.1 Continuous Random Processes
6.3.2 Discrete Random Processes
6.3.3 Continuous Random Sequencees
6.3.4 Discrete Random Sequencees
6.4 Distribution and Density Functions of Random Processes
6.4.1 Joint Distribution Functions of Random Process
6.4.2 Joint Density Functions of Random Process
6.5 Independent Random Processes
6.6 Statistical Properties of Random Processes
6.6.1 Mean
6.6.2 Autocorrelation
6.6.3 Cross Correlation
6.7 Stationary Processes
6.7.1 First-order Stationary Processes
6.7.2 Second-Order Stationary Processes
6.7.3 Wide-Sense Stationary Processes (WSS)
6.7.4 Jointly Wide-Sense Stationary Process
6.7.5 Strict-Sense Stationary Processes (SSS)
6.8 Time Averages of a Random Process
6.8.1 Time Average Function
6.8.2 Time Autocorrelation Function
6.8.3 Time Cross Correlation Function
6.9 Ergodic Theorem and Ergodic Processes
6.9.1 Ergodic Processes
6.9.2

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