Elements of Applied Stochastic ProcessesWiley, 2002 M09 6 - 488 páginas This 3rd edition of the successful Elements of Applied Stochastic Processes improves on the last edition by condensing the material and organising it into a more teachable format. It provides more in-depth coverage of Markov chains and simple Markov process and gives added emphasis to statistical inference in stochastic processes.
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Contenido
Description and Definition | 1 |
MARKOV CHAINS | 27 |
IRREDUCIBLE MARKOV CHAINS WITH ERGODIC STATES | 66 |
Derechos de autor | |
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Analysis assume Basawa behavior birth and death branching process Chapter components consider covariance function covariance stationary customers arriving death process defined derived Determine discrete discussion distribution with mean epochs equations equivalence class events occurring Example expected number exponential distribution finite Markov chain given gives identically distributed independent and identically initial interval inventory irreducible Laplace transform likelihood function limiting distribution lumpable Markov chain Markov process Markovian maximum likelihood estimates Methods node noted number of customers o(At observations obtained P₁ P₁(t parameter Pn(t Po(t Poisson process population probability density probability distribution probability-generating function problem properties queueing system recurrent renewal period renewal process sample Section servers simulation solution Solving spectral stationary process Statistical Inference stochastic process Suppose t₁ Theorem Theory transient transition probability matrix values vector zero ΠΟ