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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 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...
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 ...