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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...
Comment on "Bayesian evidence: can we beat MultiNest using traditional MCMC methods", by Rutger van Haasteren (arXiv:0911.2150)
data analysis methods statistical
2010/3/18
In arXiv:0911.2150, Rutger van Haasteren seeks to criticize the nested sampling algorithm for Bayesian data analysis in general and its MultiNest implementation in particular. He introduces a new meth...
Comparison of MCMC Methods for Estimating GARCH Models
Bayesian inference GARCH Gibbs sampler Markov chain Monte Carlo Metropolis-Hastings algorithm
2009/3/6
This paper reviews several MCMC methods for estimating the class of ARCH models, and compare performances of them. With respect to the mixing, efficiency and computational requirement of the MCMC, thi...