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» Feature Correspondence: A Markov Chain Monte Carlo Approach
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UAI
2000
13 years 8 months ago
Minimum Message Length Clustering Using Gibbs Sampling
The K-Means and EM algorithms are popular in clustering and mixture modeling due to their simplicity and ease of implementation. However, they have several significant limitations...
Ian Davidson
IJCAI
2001
13 years 9 months ago
Approximate inference for first-order probabilistic languages
A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete po...
Hanna Pasula, Stuart J. Russell
TOMACS
2011
139views more  TOMACS 2011»
13 years 2 months ago
The double CFTP method
We consider the problem of the exact simulation of random variables Z that satisfy the distributional identity Z L = V Y + (1 − V )Z, where V ∈ [0, 1] and Y are independent, an...
Luc Devroye, Lancelot F. James
SAC
2010
ACM
13 years 2 months ago
Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models
The label switching problem is caused by the likelihood of a Bayesian mixture model being invariant to permutations of the labels. The permutation can change multiple times betwee...
M. Sperrin, Thomas Jaki, E. Wit
TIP
2010
164views more  TIP 2010»
13 years 2 months ago
A Marked Point Process for Modeling Lidar Waveforms
Lidar waveforms are 1D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful ...
Clément Mallet, Florent Lafarge, Michel Rou...