Titlebook: Classical and Spatial Stochastic Processes; Rinaldo B. Schinazi Textbook 19991st edition Springer Science+Business Media New York 1999 Bra

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書目名稱Classical and Spatial Stochastic Processes影響因子(影響力)




書目名稱Classical and Spatial Stochastic Processes影響因子(影響力)學(xué)科排名




書目名稱Classical and Spatial Stochastic Processes網(wǎng)絡(luò)公開度




書目名稱Classical and Spatial Stochastic Processes網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Classical and Spatial Stochastic Processes被引頻次




書目名稱Classical and Spatial Stochastic Processes被引頻次學(xué)科排名




書目名稱Classical and Spatial Stochastic Processes年度引用




書目名稱Classical and Spatial Stochastic Processes年度引用學(xué)科排名




書目名稱Classical and Spatial Stochastic Processes讀者反饋




書目名稱Classical and Spatial Stochastic Processes讀者反饋學(xué)科排名

謊言

楓樹

ENDOW

Continuous Time Branching Random Walk,ion on a countable graph (such as . or a homogeneous tree) according to the two following rules. A particle at . waits an exponential random time with rate λ.(., .) > 0 and then gives birth to a particle at .. .(., .) are the transition probabilities of a Markov chain and λ > 0 is a parameter. A par

不安

The Contact Process on a Homogeneous Tree,The difference between the two models is that there is at most one particle per site for the contact process while there is no bound in the number of particles per site for branching Markov chains. So, branching Markov chains and the contact process may be thought of as two extreme points in the sam

脾氣暴躁的人

Lecture Notes in Computer Scienceeing the state of a certain system at time .. Given that . is in some state . then . will be in some state . with a probability denoted by .(.); the transition probabilities .(.) are built in the model. The fact that given . we may compute the distribution of . (we do not need to know the . for . <

脾氣暴躁的人

Research in Attacks, Intrusions and Defenses assume that the probability that . is in state . is .(.). Can we find a distribution . such that if . has distribution . then ., for all times ., also has distribution .? Such a distribution is said to be stationary for the chain. This chapter deals with the existence of and the convergence to stat

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晚來的提名

https://doi.org/10.1007/978-3-319-45719-2ion on a countable graph (such as . or a homogeneous tree) according to the two following rules. A particle at . waits an exponential random time with rate λ.(., .) > 0 and then gives birth to a particle at .. .(., .) are the transition probabilities of a Markov chain and λ > 0 is a parameter. A par

Allege

Adrian Dabrowski,Georg Petzl,Edgar R. WeipplThe difference between the two models is that there is at most one particle per site for the contact process while there is no bound in the number of particles per site for branching Markov chains. So, branching Markov chains and the contact process may be thought of as two extreme points in the sam