A novel Monte Carlo noise reduction operator is proposed in this paper. We apply and extend the standard bilateral filtering method and build a new local adaptive noise reduction k...
Abstract. For large state-space Markovian Decision Problems MonteCarlo planning is one of the few viable approaches to find near-optimal solutions. In this paper we introduce a new...
We propose a novel combination of techniques for robustly estimating the position of a mobile robot in outdoor environments using range data. Our approach applies a particle filte...
Today, quasi-Monte Carlo (QMC) methods are widely used in finance to price derivative securities. The QMC approach is popular because for many types of derivatives it yields an es...
Monte-Carlo evaluation consists in estimating a position by averaging the outcome of several random continuations, and can serve as an evaluation function at the leaves of a min-ma...