Marginal likelihood computation

I’ve worked on new methods to compute the marginal likelihood. It is a difficult task owing to the curse of dimensionality. However my advisors and I found that adding more samples from tempered MCMC chains sampling the posterior tails and isolating independent parameter posterior by Laplace approximation, greatly reduces the required number of independent posterior samples for convergence to the true value. The effect is more significant for higher dimension. I’ve tested multivariate Gaussian posterior up to a 12 dimension. This new methods will be introduced in another paper and used for GALPHAT galaxy morphology analysis.


~ by ilsangyoon on December 13, 2011.

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