Titlebook: Stochastic Processes; Rodney Coleman Book 1974 George Allen & Unwin Ltd 1974 Markov chain.Markov process.Poisson process.diffusion process

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书目名称Stochastic Processes影响因子(影响力)




书目名称Stochastic Processes影响因子(影响力)学科排名




书目名称Stochastic Processes网络公开度




书目名称Stochastic Processes网络公开度学科排名




书目名称Stochastic Processes被引频次




书目名称Stochastic Processes被引频次学科排名




书目名称Stochastic Processes年度引用




书目名称Stochastic Processes年度引用学科排名




书目名称Stochastic Processes读者反馈




书目名称Stochastic Processes读者反馈学科排名

Fatten

Markov Chains,A . (abbreviation: MC) is a Markov process {.. : . ∈ .} with a discrete time parameter space ., and a finite or countably infinite state space .. We take, without losing generality, both . and . to be subsets of integers.

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Diffusion Processes,These are Markov processes {.. : . ∈ .} having continuous time parameter spaces and continuous state spaces, and for which a small change in . results in only a small change in ... A realisation can be thought of as being the path of a particle moving very erratically in a continuous medium, its progress depending only on its current position.

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Overview: 978-0-04-519017-1978-94-010-9796-3

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抚慰

The Random Walk,uppose that the probability is . of any step being taken to the right, and is . = 1 - . of being to the left. Suppose also that each step is taken independently of every other step. Then this process is called the .. If the particle is in position 0 at time 0, determine the probability that it will be in position . after . steps.

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What is a Stochastic Process?,eue at time .. As time passes, customers will arrive and leave, and so the value of .. will change. At any time ., .. takes one of the values 0, 1, 2,…; and . can be any value in a subset of (−∞, ∞), the infinite past to the infinite future.