In this paper we propose a new approach to probabilistic inference on belief networks, global conditioning, which is a simple generalization of Pearl's (1986b) method of loop...
Ross D. Shachter, Stig K. Andersen, Peter Szolovit...
First-order probabilistic logic is a powerful knowledge representation language. Unfortunately, deductive reasoning based on the standard semantics for this logic does not support...
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...
Abstract. This paper proposes a new method to discriminate the vascular conditions from biological signals by using a probabilistic neural network, and develops the diagnosis suppo...
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
John D. Lafferty, Andrew McCallum, Fernando C. N. ...