Abstract. In recent years, particle filters have emerged as a useful tool that enables the application of Bayesian reasoning to problems requiring dynamic state estimation. The ef...
In this paper, a method is presented for the efficient estimation of rare-event (buffer overflow) probabilities in queueing networks using importance sampling. Unlike previously pr...
For a discrete-time finite-state Markov chain, we develop an adaptive importance sampling scheme to estimate the expected total cost before hitting a set of terminal states. This s...
To become robust, a tracking algorithm must be able to support uncertainty and ambiguity often inherently present in the data in form of occlusion and clutter. This comes usually ...
—Flow statistics is a basic task of passive measurement and has been widely used to characterize the state of the network. Adaptive Non-Linear Sampling (ANLS)is one of the most a...