AAAI
2008
13 years 6 months ago
2008
Based on Information Theory, optimal feature selection should be carried out by searching Markov blankets. In this paper, we formally analyze the current Markov blanket discovery ...
AAAI
2007
13 years 6 months ago
2007
Since Bayesian network (BN) was introduced in the field of artificial intelligence in 1980s, a number of inference algorithms have been developed for probabilistic reasoning. Ho...
AAAI
2007
13 years 6 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 ...
AAAI
2008
13 years 6 months ago
2008
Previous studies have demonstrated that encoding a Bayesian network into a SAT-CNF formula and then performing weighted model counting using a backtracking search algorithm can be...
RECOMB
2000
Springer
13 years 8 months ago
2000
Springer
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of transcription levels within the c...
ATAL
2003
Springer
13 years 8 months ago
2003
Springer
One of the factors holding back the application of multiagent, distributed approaches to large-scale sensor interpretation and diagnosis problems is the lack of good techniques fo...
ECAI
2006
Springer
13 years 8 months ago
2006
Springer
One problem faced in knowledge engineering for Bayesian networks is the exponential growth of the number of parameters in their conditional probability tables (CPTs). The most comm...
ENC
2004
IEEE
13 years 8 months ago
2004
IEEE
A method to induce bayesian networks from data to overcome some limitations of other learning algorithms is proposed. One of the main features of this method is a metric to evalua...
ISMIS
1994
Springer
13 years 8 months ago
1994
Springer
Main stream approaches in distributed artificial intelligence (DAI) are essentially logic-based. Little has been reported to explore probabilistic approach in DAI. On the other han...
NN
1997
Springer
13 years 8 months ago
1997
Springer
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...