Background: Modelling of time series data should not be an approximation of input data profiles, but rather be able to detect and evaluate dynamical changes in the time series dat...
Ryoko Morioka, Shigehiko Kanaya, Masami Y. Hirai, ...
Background: Choosing the appropriate sample size is an important step in the design of a microarray experiment, and recently methods have been proposed that estimate sample sizes ...
Complex event patterns involving Kleene closure are finding application in a variety of stream environments for tracking and monitoring purposes. In this paper, we propose a compac...
Daniel Gyllstrom, Jagrati Agrawal, Yanlei Diao, Ne...
The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we ext...
As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages that treat probability distributions as primit...