|Publisher:||Tata Mcgraw Hill|
|No. of Pages:||872|
The fourth edition of Probability, Random Variables and Stochastic Processes has been updated significantly from the previous edition, and it now includes co-author S. Unnikrishna Pillai of Polytechnic University. The book is intended for a senior/graduate level course in probability and is aimed at students in electrical engineering, math, and physics departments. The authors' approach is to develop the subject of probability theory and stochastic processes as a deductive discipline and to illustrate the theory with basic applications of engineering interest. Approximately 1/3 of the text is new material--this material maintains the style and spirit of previous editions. In order to bridge the gap between concepts and applications, a number of additional examples have been added for further clarity, as well as several new topics.
Key Features :
Changes to the fourth edition include: substantial updating of chapters 3 and 4; a new section on Parameter Estimation in chapter 8; a new section on Random Walks in chapter 10; and two new chapters (15 and 16) at the end of the book on Markov Chains and Queuing Theory.
A number of examples have been added to support the key topics, and the design of the book has been updated to allow the reader to easily locate the examples and theorems.
www.mhhe.com/papoulis -- The book's website will include a downloadable version of the solutions manual for instructors only, 200 PowerPoint slides with additional material for the reader, and a link to the author's own website.
Table of Content :
PART 1 PROBABILITY AND RANDOM VARIABLES
Chapter 1 The Meaning of Probability
Chapter 2 The Axioms of Probability
Chapter 3 Repeated Trials
Chapter 4 The Concept of a Random Variable
Chapter 5 Functions of One Random Variable
Chapter 6 Two Random Variables
Chapter 7 Sequences of Random Variables
Chapter 8 Statistics
PART 2 STOCHASTIC PROCESSES
Chapter 9 General Concepts
Chapter 10 Random Walk and Other Applications
Chapter 11 Spectral Representation
Chapter 12 Spectral Estimation
Chapter 13 Mean Square Estimation
Chapter 14 Entropy
Chapter 15 Markov Chains
Chapter 16 Markov Processes and Queueing Theory