241 search results - page 33 / 49 » Parameter learning for relational Bayesian networks |
ECML
2006
Springer
15 years 1 months ago
2006
Springer
Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this pap...
RECOMB
2003
Springer
15 years 10 months ago
2003
Springer
Genetic linkage analysis is a challenging application which requires Bayesian networks consisting of thousands of vertices. Consequently, computing the likelihood of data, which i...
KI
2007
Springer
15 years 3 months ago
2007
Springer
Abstract. Markov logic, as a highly expressive representation formalism that essentially combines the semantics of probabilistic graphical models with the full power of first-orde...
ML
2008
ACM
14 years 9 months ago
2008
ACM
Abstract. Recently, there has been an increasing interest in directed probabilistic logical models and a variety of languages for describing such models has been proposed. Although...
ECSQARU
2001
Springer
15 years 2 months ago
2001
Springer
Abstract. The K2 metric is a well-known evaluation measure (or scoring function) for learning Bayesian networks from data [7]. It is derived by assuming uniform prior distributions...