2901 search results - page 48 / 581 » Predicting non-stationary processes |
139
Voted
ASPDAC
2008
ACM
15 years 6 months ago
2008
ACM
In modern circuit design, it is difficult to provide reliable parametric yield prediction since the real distribution of process data is hard to measure. Most existing approaches ...
147
click to vote
ICML
2009
IEEE
16 years 4 months ago
2009
IEEE
We propose an analytic moment-based filter for nonlinear stochastic dynamic systems modeled by Gaussian processes. Exact expressions for the expected value and the covariance matr...
125
click to vote
ICML
2005
IEEE
16 years 4 months ago
2005
IEEE
In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relat...
160
click to vote
NIPS
2007
15 years 5 months ago
2007
In this paper we model relational random variables on the edges of a network using Gaussian processes (GPs). We describe appropriate GP priors, i.e., covariance functions, for dir...
113
click to vote
NIPS
2000
15 years 5 months ago
2000
We develop an approach for a sparse representation for Gaussian Process (GP) models in order to overcome the limitations of GPs caused by large data sets. The method is based on a...