22 citations · 30 across the 6 of their papers we have counts for
5 papers · 1 filter
Reformulating Inference Problems Through Selective Conditioning
Paul Dagum, Eric J. Horvitz
We describe how we selectively reformulate portions of a belief network that pose difficulties for solution with a stochastic-simulation algorithm. With employ the selective condit…
Dynamic Network Models for Forecasting
Paul Dagum, Adam Galper, Eric J. Horvitz
We have developed a probabilistic forecasting methodology through a synthesis of belief network models and classical time-series analysis. We present the dynamic network model (DNM…
Additive Belief-Network Models
Paul Dagum, Adam Galper
The inherent intractability of probabilistic inference has hindered the application of belief networks to large domains. Noisy OR-gates [30] and probabilistic similarity networks […
Forecasting Sleep Apnea with Dynamic Network Models
Paul Dagum, Adam Galper
Dynamic network models (DNMs) are belief networks for temporal reasoning. The DNM methodology combines techniques from time series analysis and probabilistic reasoning to provide (…
Optimal Monte Carlo Estimation of Belief Network Inference
Malcolm Pradhan, Paul Dagum
We present two Monte Carlo sampling algorithms for probabilistic inference that guarantee polynomial-time convergence for a larger class of network than current sampling algorithms…