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JMLR
2010
137views more  JMLR 2010»
13 years 3 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton
CVPR
2008
IEEE
14 years 10 months ago
Learning stick-figure models using nonparametric Bayesian priors over trees
We present a fully probabilistic stick-figure model that uses a nonparametric Bayesian distribution over trees for its structure prior. Sticks are represented by nodes in a tree i...
Edward Meeds, David A. Ross, Richard S. Zemel, Sam...
ADMA
2009
Springer
105views Data Mining» more  ADMA 2009»
13 years 11 months ago
Mining User Position Log for Construction of Personalized Activity Map
Consider a scenario in which a smart phone automatically saves the user’s positional records for personalized location-based applications. The smart phone will infer patterns of ...
Hui Fang, Wen-Jing Hsu, Larry Rudolph
IJCV
2008
201views more  IJCV 2008»
13 years 8 months ago
Probabilistic Fusion of Stereo with Color and Contrast for Bi-Layer Segmentation
This paper describes two algorithms for the real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from colour/contr...
Vladimir Kolmogorov, Antonio Criminisi, Andrew Bla...
CORR
2006
Springer
99views Education» more  CORR 2006»
13 years 8 months ago
Rational stochastic languages
In probabilistic grammatical inference, a usual goal is to infer a good approximation of an unknown distribution P called a stochastic language. The estimate of P stands in some cl...
François Denis, Yann Esposito