Previous work on context-specific independence in Bayesian networks is driven by a common goal, namely to represent the conditional probability tables in a most compact way. In th...
This paper presents two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic context free grammars (PCFGs) from terminal strings, providing an altern...
Mark Johnson, Thomas L. Griffiths, Sharon Goldwate...
Many researchers have used lexical networks and ontologies to mitigate synonymy and polysemy problems in Question Answering (QA), systems coupled with taggers, query classifiers,...
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 ...
Probabilistic inference will be of special importance when one needs to know how much we can say with what all we know given new observations. Bayesian Network is a graphical prob...