COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |
University of Cambridge > Talks.cam > fpk1's list > Stochastic networks and semimartingale reflecting Brownian motions in piecewise smooth domains
Stochastic networks and semimartingale reflecting Brownian motions in piecewise smooth domainsAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact J.L.Blackwell. Semimartingale Reflecting Brownian Motions (SRBMs) living in the closures of domains with piecewise smooth boundaries are of interest in applied probability because of their role as heavy traffic approximations for stochastic networks. Modern applications such as bandwidth sharing and packet switches can yield stochastic network models and SRBM approximations that are more general than those associated with conventional multiclass queueing networks. In justifying the approximation of a stochastic network by an SRBM , a crucial step is the use of a perturbation result or invariance principle. In essence this implies that a process satisfying the definition of an SRBM , except for small random perturbations in the defining conditions, is close in distribution to an SRBM . Here we describe sufficient conditions for an invariance principle to hold for SRB Ms in piecewise smooth domains. A crucial ingredient in the proof of this result is an oscillation inequality for solutions of a perturbed Skorokhod problem. Some applications of our results and open problems will be discussed. This talk is part of the fpk1's list series. This talk is included in these lists:Note that ex-directory lists are not shown. |
Other listsEA: Cambridge Power and VisionOther talks'Honouring Giulio Regeni: a plea for research in risky environments' Neural Networks and Natural Language Processing Social Representations of Women who Live as Men in Northern Albania A V HILL LECTURE - The cortex and the hand of the primate: a special relationship Random Feature Expansions for Deep Gaussian Processes DataFlow SuperComputing for BigData To be confirmed Throwing light on organocatalysis: new opportunities in enantioselective synthesis Cyclic Peptides: Building Blocks for Supramolecular Designs Embedding Musical Codes into an Interactive Piano Composition |