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» Causal inference using the algorithmic Markov condition
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MM
2005
ACM
140views Multimedia» more  MM 2005»
15 years 3 months ago
Topic transition detection using hierarchical hidden Markov and semi-Markov models
In this paper we introduce a probabilistic framework to exploit hierarchy, structure sharing and duration information for topic transition detection in videos. Our probabilistic d...
Dinh Q. Phung, Thi V. Duong, Svetha Venkatesh, Hun...
TIP
2008
133views more  TIP 2008»
14 years 9 months ago
A Recursive Model-Reduction Method for Approximate Inference in Gaussian Markov Random Fields
This paper presents recursive cavity modeling--a principled, tractable approach to approximate, near-optimal inference for large Gauss-Markov random fields. The main idea is to su...
Jason K. Johnson, Alan S. Willsky
ICIP
2002
IEEE
15 years 11 months ago
Parametric contour tracking using unscented Kalman filter
This paper presents an efficient method to integrate various spatial-temporal constraints to regularize the contour tracking. The global shape of the contour is represented in a p...
Yunqiang Chen, Thomas S. Huang, Yong Rui
JMLR
2006
190views more  JMLR 2006»
14 years 9 months ago
Causal Graph Based Decomposition of Factored MDPs
We present Variable Influence Structure Analysis, or VISA, an algorithm that performs hierarchical decomposition of factored Markov decision processes. VISA uses a dynamic Bayesia...
Anders Jonsson, Andrew G. Barto
UAI
2003
14 years 11 months ago
Robust Independence Testing for Constraint-Based Learning of Causal Structure
This paper considers a method that combines ideas from Bayesian learning, Bayesian network inference, and classical hypothesis testing to produce a more reliable and robust test o...
Denver Dash, Marek J. Druzdzel