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NIPS
2007
13 years 9 months ago
Discovering Weakly-Interacting Factors in a Complex Stochastic Process
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
Charlie Frogner, Avi Pfeffer
FLAIRS
2004
13 years 9 months ago
An Empirical Study of Probability Elicitation Under Noisy-OR Assumption
Bayesian network is a popular modeling tool for uncertain domains that provides a compact representation of a joint probability distribution among a set of variables. Even though ...
Adam Zagorecki, Marek J. Druzdzel
IAAI
2003
13 years 9 months ago
A Probabilistic Vehicle Diagnostic System Using Multiple Models
In addition to being accurate, it is important that diagnostic systems for use in automobiles also have low development and hardware costs. Model-based methods have shown promise ...
Matthew L. Schwall, J. Christian Gerdes, Bernard B...
UAI
2004
13 years 9 months ago
Iterative Conditional Fitting for Gaussian Ancestral Graph Models
Ancestral graph models, introduced by Richardson and Spirtes (2002), generalize both Markov random fields and Bayesian networks to a class of graphs with a global Markov property ...
Mathias Drton, Thomas S. Richardson
UAI
2003
13 years 9 months ago
Probabilistic Models For Joint Clustering And Time-Warping Of Multidimensional Curves
In this paper we present a family of models and learning algorithms that can simultaneously align and cluster sets of multidimensional curves measured on a discrete time grid. Our...
Darya Chudova, Scott Gaffney, Padhraic Smyth