CORR
2008
Springer
13 years 5 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 5 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 5 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 5 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 5 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
13 years 6 months 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
13 years 6 months 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
13 years 6 months 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
13 years 6 months 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
13 years 6 months 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 ...