88 search results - page 3 / 18 » Gaussian process product models for nonparametric nonstation... |
ESSMAC
2003
Springer
13 years 10 months ago
2003
Springer
Abstract. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. The Gaussian processes can h...
ICML
2004
IEEE
14 years 6 months ago
2004
IEEE
Computer experiments often require dense sweeps over input parameters to obtain a qualitative understanding of their response. Such sweeps can be prohibitively expensive, and are ...
ESSMAC
2003
Springer
13 years 10 months ago
2003
Springer
Kernel-based non-parametric models have been applied widely over recent years. However, the associated computational complexity imposes limitations on the applicability of those me...
ESANN
2007
13 years 6 months ago
2007
In a previous work, we developed a quasi-efficient maximum likelihood approach for blindly separating stationary, temporally correlated sources modeled by Markov processes. In this...
JCST
2010
13 years 3 months ago
2010
In the Bayesian mixture modeling framework it is possible to infer the necessary number of components to model the data and therefore it is unnecessary to explicitly restrict the n...