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112
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NIPS
2004
15 years 5 months ago
Sampling Methods for Unsupervised Learning
We present an algorithm to overcome the local maxima problem in estimating the parameters of mixture models. It combines existing approaches from both EM and a robust fitting algo...
Robert Fergus, Andrew Zisserman, Pietro Perona
ISMB
1993
15 years 5 months ago
Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families
A Bayesian method for estimating the amino acid distributions in the states of a hidden Markov model (HMM) for a protein familyor the columns of a multiple alignment of that famil...
Michael Brown, Richard Hughey, Anders Krogh, I. Sa...
131
Voted
SAC
2008
ACM
15 years 3 months ago
Adaptive importance sampling in general mixture classes
In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the p...
Olivier Cappé, Randal Douc, Arnaud Guillin,...
129
Voted
MOC
2010
14 years 10 months ago
Convergent finite element discretization of the multi-fluid nonstationary incompressible magnetohydrodynamics equations
Abstract. We propose a convergent implicit stabilized finite element discretization of the nonstationary incompressible magnetohydrodynamics equations with variable density, viscos...
Lubomír Bañas, Andreas Prohl
118
Voted
EUROCRYPT
2007
Springer
15 years 10 months ago
Conditional Computational Entropy, or Toward Separating Pseudoentropy from Compressibility
We study conditional computational entropy: the amount of randomness a distribution appears to have to a computationally bounded observer who is given some correlated information....
Chun-Yuan Hsiao, Chi-Jen Lu, Leonid Reyzin