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JMLR
2010
137views more  JMLR 2010»
14 years 9 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
16 years 5 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»
15 years 6 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»
15 years 3 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»
15 years 3 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