Probability, Random Variables, and Stochastic ProcessesThe 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. |
Contents
The Meaning of Probability | 3 |
The Axioms of Probability | 15 |
Repeated Trials | 46 |
Copyright | |
14 other sections not shown
Other editions - View all
Probability, Random Variables, and Stochastic Processes Athanasios Papoulis,S. Unnikrishna Pillai No preview available - 2002 |
Probability, random variables, and stochastic processes Athanasios Papoulis No preview available - 2002 |
Probability, Random Variables, and Stochastic Processes Athanasios Papoulis,S. Unnikrishna Pillai No preview available - 2002 |
Common terms and phrases
a₁ assume autocorrelation autocovariance average coefficients conclude conditional conditional entropy constant corresponding denote density determine distribution elements entropy equals equation error event EXAMPLE expected values experiment exponential exponential distribution FIGURE filter finite follows Fourier function fx(x fy(y given H₁ Hence input integral interval k₁ linear Markov chain matrix moment generating function obtain orthogonal outcomes output P₁ parameter partition Poisson distributed Poisson process power spectrum problem process x(t Proof queue random number sequence random walk represents S₁ samples server solution staircase function stationary process statistics stochastic process Suppose t₁ theorem total number transform trials unbiased estimator variance vector white noise x₁ y₁ yields zero mean