In many pattern recognition/classification problem the true class conditional model and class probabilities are approximated for reasons of reducing complexity and/or of statistic...
A common assumption in supervised learning is that the training and test input points follow the same probability distribution. However, this assumption is not fulfilled, e.g., in...
We study an approximation for the zero-variance change of measure to estimate the probability of a rare event in a continuous-time Markov chain. The rare event occurs when the cha...
Pieter-Tjerk de Boer, Pierre L'Ecuyer, Gerardo Rub...
Abstract. We propose a framework in which query sizes can be estimated from arbitrary statistical assertions on the data. In its most general form, a statistical assertion states t...
In this paper, we propose a general two-dimensional hidden Markov model (2D-HMM), where dependency of the state transition probability on any state is allowed as long as causality...