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» Speeeding Up Markov Chain Monte Carlo Algorithms
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NAACL
2010
14 years 10 months ago
Variational Inference for Adaptor Grammars
Adaptor grammars extend probabilistic context-free grammars to define prior distributions over trees with "rich get richer" dynamics. Inference for adaptor grammars seek...
Shay B. Cohen, David M. Blei, Noah A. Smith
138
Voted
JMLR
2010
173views more  JMLR 2010»
14 years 7 months ago
Elliptical slice sampling
Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Mo...
Iain Murray, Ryan Prescott Adams, David J. C. MacK...
118
Voted
CVPR
2007
IEEE
16 years 2 months ago
Metropolis-Hasting techniques for finite-element-based registration
In this paper, we focus on the design of Markov Chain Monte Carlo techniques in a statistical registration framework based on finite element basis (FE). Due to the use of FE basis...
Adeline M. M. Samson, Frédéric J. P....
BMCBI
2007
127views more  BMCBI 2007»
15 years 23 days ago
A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments
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...
Hyungwon Choi, Ronglai Shen, Arul M. Chinnaiyan, D...
120
Voted
IJCAI
2001
15 years 2 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