We extend stochastic network optimization theory to treat networks with arbitrary sample paths for arrivals, channels, and mobility. The network can experience unexpected link or n...
Calvin and Nakayama previously introduced permuting as a way of improving existing standardized time series methods. The basic idea is to split a simulated sample path into nonove...
We investigate a family of inference problems on Markov models, where many sample paths are drawn from a Markov chain and partial information is revealed to an observer who attemp...
Daniel Sheldon, M. A. Saleh Elmohamed, Dexter Koze...