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Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Parallel Gaussian Process Regression Low-Rank Covariance Matrix Approximations
2013/6/14
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due ...
Adaptive Markov Chain Monte Carlo for Auxiliary Variable Method and Its Application to Parallel Tempering
Adaptive Markov Chain Monte Carlo Auxiliary Variable Method Parallel Tempering Conver-gence
2012/9/19
Auxiliary variable methods such as the Parallel Tempering and the cluster Monte Carlo methods generate samples that follow a target distri-bution by using proposal and auxiliary distributions.In sampl...
Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs
Machine Learning (stat.ML) Neural and Evolutionary Computing (cs.NE)
2010/12/17
Restricted Boltzmann Machines (RBM) have attracted a lot of attention of late, as one the principle building blocks of deep networks. Training RBMs remains problematic however, because of the intracti...
Using parallel computation to improve Independent Metropolis--Hastings based estimation
MCMC algorithm independent Metropolis{Hastings
2010/10/19
In this paper, we consider the implications of the fact that parallel raw-power can be exploited by a generic Metropolis--Hastings algorithm if the proposed values are independent. In particular, we p...
Efficient Monte Carlo sampling by parallel marginalization
Markov chain Monte Carlo renormalization multi-grid filtering parameterestimation
2010/4/26
Markov chain Monte Carlo sampling methods often suffer from long
correlation times. Consequently, these methods must be run for
many steps to generate an independent sample. In this paper a
method ...