Stochastic Processes and Their ApplicationsCRC Press, 18 de out. de 2001 - 338 páginas This book introduces stochastic processes and their applications for students in engineering, industrial statistics, science, operations research, business, and finance. It provides the theoretical foundations for modeling time-dependent random phenomena encountered in these disciplines. Through numerous science and engineering-based examples and exercises, the author presents the subject in a comprehensible, practically oriented way, but he also includes some important proofs and theoretically challenging examples and exercises that will appeal to more mathematically minded readers. Solutions to most of the exercises are included either in an appendix or within the text. |
Conteúdo
1 | 11 |
1 | 18 |
Stochastic Processes | 43 |
4 | 50 |
Exercises | 67 |
1 | 99 |
V | 111 |
8 | 123 |
6589 | 202 |
Applications in Queueing Theory | 209 |
9 | 241 |
7 | 251 |
Spectral Analysis of Stationary Processes | 293 |
Landau Order Symbol | 312 |
| 319 | |
DiscreteTime Markov Chains | 137 |
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Termos e frases comuns
assumed condition continuous-time corresponding covariance function cycle lengths death process defined definition denotes differential equations discrete random variables distributed with parameter distribution function equivalent Erlang distribution example expected value exponentially distributed failure rate Figure finite formula given Hence homogeneous Poisson process identically distributed increments interval Laplace-transform lifetime Markov chain node normally distributed number of customers obtained one-dimensional ordinary renewal process Ornstein-Uhlenbeck process particle Po(t probability distribution process with drift process with intensity properties queueing network queueing system random experiment random sequence random vector renewal function respectively sample paths satisfies semi-Markov process server space spectral density stationary process stationary state probabilities stochastic process X(t theorem tion transition graph transition matrix transition probabilities transition rates trend function type 1-customer Var(X variables with parameter variance white noise wide-sense stationary Wiener process X₁ Y₁ yields z-transform λι πο