CORR
2008
Springer
13 years 11 months ago
2008
Springer
We propose an approach to lossy source coding, utilizing ideas from Gibbs sampling, simulated annealing, and Markov Chain Monte Carlo (MCMC). The idea is to sample a reconstructio...
CSDA
2010
13 years 11 months ago
2010
We consider mixtures of parametric densities on the positive reals with a normalized generalized gamma process (Brix, 1999) as mixing measure. This class of mixtures encompasses t...
CSDA
2010
13 years 11 months ago
2010
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...
CORR
2008
Springer
13 years 11 months ago
2008
Springer
As an example of the recently introduced concept of rate of innovation, signals that are linear combinations of a finite number of Diracs per unit time can be acquired by linear fi...
BMCBI
2007
13 years 11 months ago
2007
Background: With the explosion in data generated using microarray technology by different investigators working on similar experiments, it is of interest to combine results across...
UAI
1996
14 years 25 days ago
1996
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
UAI
2000
14 years 26 days ago
2000
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...
WSC
1998
14 years 26 days ago
1998
In simulation modeling and analysis, there are two situations where there is uncertainty about the number of parameters needed to specify a model. The first is in input modeling w...
BMVC
2000
14 years 26 days ago
2000
We tackle the problem of object recognition using a Bayesian approach. A marked point process [1] is used as a prior model for the (unknown number of) objects. A sample is generat...
NIPS
2003
14 years 26 days ago
2003
In models that define probabilities via energies, maximum likelihood learning typically involves using Markov Chain Monte Carlo to sample from the model’s distribution. If the ...