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MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
Gaussian Process Models and Interpolators for Deterministic Computer Simulators
Computer experiment Matrix inverse approximation Regularization
2010/3/11
For many expensive deterministic computer simulators, the outputs do not have replication
error and the desired metamodel (or emulator) is an interpolator of the observed data. Realizations
of Gauss...
Estimation of Faraday Rotation Measures of the Near Galactic Sky Using Gaussian Process Models
Markov chain Monte Carlo Gaussian process error mixture model spatial model
2009/9/22
Our primary goal is to obtain a smoothed summary estimate of the
magnetic eld generated in and near to the Milky Way by using Faraday rotation
measures (RMs) Each RM in our data set provides an inte...