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» A Differential Approach to Inference in Bayesian Networks
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JMLR
2010
140views more  JMLR 2010»
13 years 2 months ago
Mean Field Variational Approximation for Continuous-Time Bayesian Networks
Continuous-time Bayesian networks is a natural structured representation language for multicomponent stochastic processes that evolve continuously over time. Despite the compact r...
Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman
JCB
2006
185views more  JCB 2006»
13 years 7 months ago
Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models
As postgenomic biology becomes more predictive, the ability to infer rate parameters of genetic and biochemical networks will become increasingly important. In this paper, we expl...
Andrew Golightly, Darren J. Wilkinson
AAAI
2006
13 years 8 months ago
Performing Incremental Bayesian Inference by Dynamic Model Counting
The ability to update the structure of a Bayesian network when new data becomes available is crucial for building adaptive systems. Recent work by Sang, Beame, and Kautz (AAAI 200...
Wei Li 0002, Peter van Beek, Pascal Poupart
AI
2006
Springer
13 years 11 months ago
An Improved LAZY-AR Approach to Bayesian Network Inference
We propose LAZY arc-reversal with variable elimination (LAZY-ARVE) as a new approach to probabilistic inference in Bayesian networks (BNs). LAZY-ARVE is an improvement upon LAZY ar...
Cory J. Butz, Shan Hua
JAIR
2011
129views more  JAIR 2011»
13 years 2 months ago
Exploiting Structure in Weighted Model Counting Approaches to Probabilistic Inference
Previous studies have demonstrated that encoding a Bayesian network into a SAT formula and then performing weighted model counting using a backtracking search algorithm can be an ...
Wei Li 0002, Pascal Poupart, Peter van Beek