Probability and Random ProcessesA resource for probability AND random processes, with hundreds ofworked examples and probability and Fourier transform tables This survival guide in probability and random processes eliminatesthe need to pore through several resources to find a certainformula or table. It offers a compendium of most distributionfunctions used by communication engineers, queuing theoryspecialists, signal processing engineers, biomedical engineers,physicists, and students. Key topics covered include: * Random variables and most of their frequently used discrete andcontinuous probability distribution functions * Moments, transformations, and convergences of randomvariables * Characteristic, generating, and moment-generating functions * Computer generation of random variates * Estimation theory and the associated orthogonalityprinciple * Linear vector spaces and matrix theory with vector and matrixdifferentiation concepts * Vector random variables * Random processes and stationarity concepts * Extensive classification of random processes * Random processes through linear systems and the associated Wienerand Kalman filters * Application of probability in single photon emission tomography(SPECT) More than 400 figures drawn to scale assist readers inunderstanding and applying theory. Many of these figures accompanythe more than 300 examples given to help readers visualize how tosolve the problem at hand. In many instances, worked examples aresolved with more than one approach to illustrate how differentprobability methodologies can work for the same problem. Several probability tables with accuracy up to nine decimal placesare provided in the appendices for quick reference. A specialfeature is the graphical presentation of the commonly occurringFourier transforms, where both time and frequency functions aredrawn to scale. This book is of particular value to undergraduate and graduatestudents in electrical, computer, and civil engineering, as well asstudents in physics and applied mathematics. Engineers, computerscientists, biostatisticians, and researchers in communicationswill also benefit from having a single resource to address mostissues in probability and random processes. |
Contents
CHAPTER 1 Sets Fields and Events | 1 |
CHAPTER 2 Probability Space and Axioms | 10 |
CHAPTER 3 Basic Combinatorics | 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 |
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Common terms and phrases
¼ ¼ ¼ À1 ¼ ð autocorrelation function autocovariance balls binomial binomial distribution coefficients conditional density conditional expectation conditional probability corresponding covariance Cx(h Cx(T defined density function Differentiating digit discrete distribution function eigenvalues equation estimator event Example ffiffi ffiffiffi ffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffiffi FIGURE filter follows Fourier function FX(x fX(x fXY x,y fY(y fZ(z Gaussian random given by Eq Hence histogram Hopt independent integral interval inverse Jmin joint density Kalman filter Markov Markov chain matrix mean value minimum mean-square error noise obtained from Eq orthogonal output parameter Poisson random process random process X(t random variable region result RX(t sample space sequence shown in Fig sin(vt solve stationary process Substituting Eq Sx(w t₁ Table transformation unbiased vector Wiener process zero mean þ v2 μχ