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ISAAC
2003
Springer
101views Algorithms» more  ISAAC 2003»
15 years 2 months ago
Rapid Mixing of Several Markov Chains for a Hard-Core Model
The mixing properties of several Markov chains to sample from configurations of a hard-core model have been examined. The model is familiar in the statistical physics of the liqui...
Ravi Kannan, Michael W. Mahoney, Ravi Montenegro
CVPR
2009
IEEE
1216views Computer Vision» more  CVPR 2009»
16 years 4 months ago
Marked Point Processes for Crowd Counting
A Bayesian marked point process (MPP) model is developed to detect and count people in crowded scenes. The model couples a spatial stochastic process governing number and placem...
Robert T. Collins, Weina Ge
CSDA
2010
118views more  CSDA 2010»
14 years 9 months ago
Grapham: Graphical models with adaptive random walk Metropolis algorithms
Recently developed adaptive Markov chain Monte Carlo (MCMC) methods have been applied successfully to many problems in Bayesian statistics. Grapham is a new open source implementat...
Matti Vihola
AAAI
2006
14 years 11 months ago
Probabilistic Self-Localization for Sensor Networks
This paper describes a technique for the probabilistic self-localization of a sensor network based on noisy inter-sensor range data. Our method is based on a number of parallel in...
Dimitri Marinakis, Gregory Dudek
JMLR
2010
145views more  JMLR 2010»
14 years 4 months ago
Parallelizable Sampling of Markov Random Fields
Markov Random Fields (MRFs) are an important class of probabilistic models which are used for density estimation, classification, denoising, and for constructing Deep Belief Netwo...
James Martens, Ilya Sutskever