## Probability, Random Variables, and Stochastic Processes* Treats probability theory and stochastic processes as a deductive discipline and illustrates them with basic engineering applications * Approximately 1/3 of the text is new with new material on: Parameter Estimation, Random Walks, Markov Chains, and Queuing Theory |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

The Axioms of Probability | 15 |

Repeated Trials | 37 |

The Concept of a Random Variable | 63 |

Copyright | |

10 other sections not shown

### Other editions - View all

### Common terms and phrases

arbitrary assume assumption autocorrelation average binary characteristic function Clearly coefficients coin conclude conditional density conditional entropy consists constant correlation corresponding cyclostationary defined definition denote differential discrete type discrete-time elements entropy rate equals equation error estimate event Example expected values experiment Figure filter find the probability follows Fourier transform Frequency interpretation Furthermore Fx(x fy(y given Hence independent input Inserting integral interval inverse joint density jointly normal length Markoff matrix obtain orthogonal outcomes output parameter Poisson points power spectrum predictor Prob problem process x(f Proof properties RVs x S(co sample satisfies sequence shot noise signal solution specified spectra staircase function stationary process statistics stochastic process subsets Suppose takes the values theorem theory tossing trials variance vector white noise Wiener process WSS process yields zero mean