We study two-layer belief networks of binary random variables in which the conditional probabilities Pr childjparents depend monotonically on weighted sums of the parents. In larg...
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 ...
We present the Vida family of abstractions of anonymous communication systems, model them probabilistically and apply Bayesian inference to extract patterns of communications and u...
We introduce cube summing, a technique that permits dynamic programming algorithms for summing over structures (like the forward and inside algorithms) to be extended with non-loc...
METAI is a generic process calculus that can be instantiated by supplying rewriting rules defining an operational semantics to make numerous process calculi such as the -calculus,...