53 search results - page 6 / 11 » Expectation Propagation for approximate Bayesian inference |
NIPS
1998
13 years 9 months ago
1998
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
ICML
2010
IEEE
13 years 8 months ago
2010
IEEE
Many temporal processes can be naturally modeled as a stochastic system that evolves continuously over time. The representation language of continuous-time Bayesian networks allow...
IJAR
2006
13 years 7 months ago
2006
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for solving hybrid Bayesian networks. Any probability density function can be approximated...
WWW
2009
ACM
14 years 8 months ago
2009
ACM
We present a probabilistic model for generating personalised recommendations of items to users of a web service. The Matchbox system makes use of content information in the form o...
AAAI
2007
13 years 10 months ago
2007
In clique tree clustering, inference consists of propagation in a clique tree compiled from a Bayesian network. In this paper, we develop an analytical approach to characterizing ...