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ICML
2004
IEEE
14 years 8 months ago
Learning Bayesian network classifiers by maximizing conditional likelihood
Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when lear...
Daniel Grossman, Pedro Domingos
AAAI
2008
13 years 10 months ago
Factored Models for Probabilistic Modal Logic
Modal logic represents knowledge that agents have about other agents' knowledge. Probabilistic modal logic further captures probabilistic beliefs about probabilistic beliefs....
Afsaneh Shirazi, Eyal Amir
ECAI
2010
Springer
13 years 8 months ago
Context-Specific Independence in Directed Relational Probabilistic Models and its Influence on the Efficiency of Gibbs Sampling
Abstract. There is currently a large interest in relational probabilistic models. While the concept of context-specific independence (CSI) has been well-studied for models such as ...
Daan Fierens
VLSID
2005
IEEE
255views VLSI» more  VLSID 2005»
14 years 8 months ago
Estimation of Switching Activity in Sequential Circuits Using Dynamic Bayesian Networks
We propose a novel, non-simulative, probabilistic model for switching activity in sequential circuits, capturing both spatio-temporal correlations at internal nodes and higher ord...
Sanjukta Bhanja, Karthikeyan Lingasubramanian, N. ...
ISMIS
2000
Springer
13 years 11 months ago
A Comparative Study of Noncontextual and Contextual Dependencies
There is current interest in generalizing Bayesian networks by using dependencies which are more general than probabilistic conditional independence (CI). Contextual dependencies, ...
S. K. Michael Wong, Cory J. Butz