22 search results - page 1 / 5 » Bayesian Learning in Undirected Graphical Models: Approximat... |
UAI
2004
13 years 6 months ago
2004
Bayesian learning in undirected graphical models--computing posterior distributions over parameters and predictive quantities-is exceptionally difficult. We conjecture that for ge...
ICML
2009
IEEE
14 years 5 months ago
2009
IEEE
In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the dependency structure than directed graphical mode...
FUZZIEEE
2007
IEEE
13 years 11 months ago
2007
IEEE
—Approaches based on conditional independence tests are among the most popular methods for learning graphical models from data. Due to the predominance of Bayesian networks in th...
NIPS
2008
13 years 6 months ago
2008
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
AMAI
2008
Springer
13 years 5 months ago
2008
Springer
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayesian nets (BNs). Prior distributions are defined using stochastic logic programs...