Generating Markov-Chain Transitions Quickly: I
Copyright policy )Generating Markov-Chain Transitions Quickly: I
We give a way to generate transitions directly from a compact representation of the generator of a continuous-time Markov chain corresponding to a class that includes many queueing networks and reliability problems. Under specified conditions, this is (provably) faster than generating these transitions via a future-event schedule. Under weaker (specified) conditions, a variant is never slower but, when the original conditions hold, will be faster. This increases efficiency of certain simulations by an order of magnitude. We show that the acceptance-complement method is not competitive in our context. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.
- University of Colorado System United States
queueing networks, Applications of queueing theory (congestion, allocation, storage, traffic, etc.), Markov chain, simulations, Markov chains (discrete-time Markov processes on discrete state spaces), Queueing theory (aspects of probability theory), reliability problems
queueing networks, Applications of queueing theory (congestion, allocation, storage, traffic, etc.), Markov chain, simulations, Markov chains (discrete-time Markov processes on discrete state spaces), Queueing theory (aspects of probability theory), reliability problems
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).11 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!