ECAI
2010
Springer
13 years 5 months ago
2010
Springer
We investigate probabilistic propositional logic as a way of expressing and reasoning about uncertainty. In contrast to Bayesian networks, a logical approach can easily cope with i...
UAI
2007
13 years 5 months ago
2007
The traditional approach to building Bayesian networks is to build the graphical structure using a graphical editor and then add probabilities using a separate spreadsheet for eac...
UAI
1996
13 years 5 months ago
1996
In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly ...
UAI
1997
13 years 5 months ago
1997
There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynam...
UAI
1997
13 years 5 months ago
1997
Recently several researchers have investigated techniques for using data to learn Bayesian networks containing compact representations for the conditional probability distribution...
UAI
1997
13 years 5 months ago
1997
This paper re-examines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in on-line learning [1...
UAI
2000
13 years 5 months ago
2000
Algorithms for learning the conditional probabilities of Bayesian networks with hidden variables typically operate within a high-dimensional search space and yield only locally op...
NIPS
2000
13 years 5 months ago
2000
Bayesian networks are graphical representations of probability distributions. In virtually all of the work on learning these networks, the assumption is that we are presented with...
UAI
1998
13 years 5 months ago
1998
In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a beli...
FLAIRS
2000
13 years 5 months ago
2000
Multiply Sectioned Bayesian Networks (MSBN)provide a coherence framework for multi-agent distributed interpretation tasks. Duringthe construction or dynamicformation of an MSBN,au...