Abstract. We introduce a new genetic algorithm approach for learning a Bayesian network structure from data. Our method is capable of learning over all node orderings and structure...
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...
The implementation of Bayesian predictive procedures under standard normal models is considered. Two distributions are of particular interest, the K-prime and Ksquare distribution...
We describe a Bayesian inference algorithm that can be used to train any cascade of weighted finite-state transducers on end-toend data. We also investigate the problem of automat...
David Chiang, Jonathan Graehl, Kevin Knight, Adam ...
ABSTRACT the pyramid. Recently, a number of promising quanMultiresolution imagedecompositions (e. g., wavelets), in conjunction with a variety of quantization schemes, have been sh...
Birsen Yazici, Mary L. Comer, Rangasami L. Kashyap...