Abstract We derive an explicit representation of the transitions of the Heston stochastic volatility model and use it for fast and accurate simulation of the model. Of particular i...
We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
We extend previous work on fully unsupervised part-of-speech tagging. Using a non-parametric version of the HMM, called the infinite HMM (iHMM), we address the problem of choosing...
Jurgen Van Gael, Andreas Vlachos, Zoubin Ghahraman...
In this paper, we shall address the issue of semantic extraction of different regions of interest. The proposed approach is based on statistical methods and models inspired from l...
— Recent studies on wireless sensor networks (WSN) have shown that the duration of contacts and inter-contacts are power law distributed. While this is a strong property of these...