## Probability and Random ProcessesA resource for probability AND random processes, with hundreds of worked examples and probability and Fourier transform tables This survival guide in probability and random processes eliminates the need to pore through several resources to find a certain formula or table. It offers a compendium of most distribution functions used by communication engineers, queuing theory specialists, signal processing engineers, biomedical engineers, physicists, and students. Key topics covered include: * Random variables and most of their frequently used discrete and continuous probability distribution functions * Moments, transformations, and convergences of random variables * Characteristic, generating, and moment-generating functions * Computer generation of random variates * Estimation theory and the associated orthogonality principle * Linear vector spaces and matrix theory with vector and matrix differentiation concepts * Vector random variables * Random processes and stationarity concepts * Extensive classification of random processes * Random processes through linear systems and the associated Wiener and Kalman filters * Application of probability in single photon emission tomography (SPECT) More than 400 figures drawn to scale assist readers in understanding and applying theory. Many of these figures accompany the more than 300 examples given to help readers visualize how to solve the problem at hand. In many instances, worked examples are solved with more than one approach to illustrate how different probability methodologies can work for the same problem. Several probability tables with accuracy up to nine decimal places are provided in the appendices for quick reference. A special feature is the graphical presentation of the commonly occurring Fourier transforms, where both time and frequency functions are drawn to scale. This book is of particular value to undergraduate and graduate students in electrical, computer, and civil engineering, as well as students in physics and applied mathematics. Engineers, computer scientists, biostatisticians, and researchers in communications will also benefit from having a single resource to address most issues in probability and random processes. |

### What people are saying - Write a review

User Review - Flag as inappropriate

fhxn

### Contents

1 | |

10 | |

25 | |

CHAPTER 4 Discrete Distributions | 37 |

CHAPTER 5 Random Variables | 64 |

CHAPTER 6 Continuous Random Variables and Basic Distributions | 79 |

CHAPTER 7 Other Continuous Distributions | 95 |

CHAPTER 8 Conditional Densities and Distributions | 122 |

CHAPTER 15 Computer Methods for Generating Random Variates | 264 |

CHAPTER 16 Elements of Matrix Algebra | 284 |

CHAPTER 17 Random Vectors and MeanSquare Estimation | 311 |

CHAPTER 18 Estimation Theory | 340 |

CHAPTER 19 Random Processes | 406 |

CHAPTER 20 Classification of Random Processes | 490 |

CHAPTER 21 Random Processes and Linear Systems | 574 |

CHAPTER 22 Weiner and Kalman Filters | 625 |

CHAPTER 9 Joint Densities and Distributions | 135 |

CHAPTER 10 Moments and Conditional Moments | 146 |

CHAPTER 11 Characteristic Functions and Generating Functions | 155 |

CHAPTER 12 Functions of a Single Random Variable | 173 |

CHAPTER 13 Functions of Multiple Random Variables | 206 |

CHAPTER 14 Inequalities Convergences and Limit Theorems | 241 |

CHAPTER 23 Probabilistic Methods in Transmission Tomography | 666 |

APPENDIXES | 683 |

714 | |

716 | |

### Other editions - View all

### Common terms and phrases

ผ ๐ 2006 John Wiley มมม algorithm autocorrelation function autocovariance balls binomial distribution coefﬁcients conditional density conditional expectation conditional probability corresponding covariance CX(h CX(T deﬁned deﬁnition degrees of freedom determine Differentiating digit discrete distribution function eigenvalues equation event Example F X(x ffiffiffi ffiffiffiffi ffiffiffiffiffiffi 2p ffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffiffi FIGURE ﬁlter ﬁnd ﬁrst follows fX(x fXY(x,y fY(y fZ(z fZW(z,w Gaussian distribution given by Eq Hence histogram impulse response input integral interval inverse Kalman linear Markov chain martingale mean and variance mean value minimum mean-square error noise obtained from Eq orthogonal output parameter pixel Poisson Poisson distribution Probability and Random random process random process X(t region regression result rX(h RX(t RXY(t sample space satisﬁed sequence shown in Fig solution solve stationary process stationary random process Substituting Eq Table uniformly distributed vector Venkatarama Krishnan Copyright Wiener process zero mean

### Popular passages

Page 5 - Finally, the difference of two sets A and B, denoted by A — B, is the set of all elements of A that are not elements of B. A — B = {x: xe A and x $ B} For example, {1,3, 9} -{3, 5, 7} = {1,9}.