Sciweavers

UAI
2003
13 years 5 months ago
An Importance Sampling Algorithm Based on Evidence Pre-propagation
Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the E...
Changhe Yuan, Marek J. Druzdzel
UAI
2003
13 years 5 months ago
Learning Module Networks
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...
UAI
2003
13 years 5 months ago
Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
Uri Nodelman, Christian R. Shelton, Daphne Koller
UAI
2003
13 years 5 months ago
Updating with incomplete observations
Currently, there is renewed interest in the problem, raised by Shafer in 1985, of updating probabilities when observations are incomplete (or setvalued). This is a fundamental pro...
Gert de Cooman, Marco Zaffalon
AAAI
1998
13 years 5 months ago
Probabilistic Frame-Based Systems
Two of the most important threads of work in knowledge representation today are frame-based representation systems (FRS's) and Bayesian networks (BNs). FRS's provide an ...
Daphne Koller, Avi Pfeffer
AAAI
1998
13 years 5 months ago
Structured Representation of Complex Stochastic Systems
This paperconsidersthe problem of representingcomplex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic proce...
Nir Friedman, Daphne Koller, Avi Pfeffer
UAI
2004
13 years 5 months ago
A New Characterization of Probabilities in Bayesian Networks
We characterize probabilities in Bayesian networks in terms of algebraic expressions called quasi-probabilities. These are arrived at by casting Bayesian networks as noisy AND-OR-...
Lenhart K. Schubert
UAI
2004
13 years 5 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
2004
13 years 5 months ago
Bayesian Biosurveillance of Disease Outbreaks
Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal pat...
Gregory F. Cooper, Denver Dash, John Levander, Wen...
UAI
2004
13 years 5 months ago
Annealed MAP
Maximum a Posteriori assignment (MAP) is the problem of finding the most probable instantiation of a set of variables given the partial evidence on the other variables in a Bayesi...
Changhe Yuan, Tsai-Ching Lu, Marek J. Druzdzel