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Is the Gaussian-prior assumption too rigit? Most of the time, flexible likelihoods, averaged posterior draws over MCMC samples, and GPs over parameters of a more flexible model of distirbuions will suffice. If not, GPs can easily be extended to more general Gaussian-mixture-priors.
Is the Gaussian-prior assumption too rigit? Most of the time, flexible likelihoods, averaged posterior draws over MCMC samples, and GPs over parameters of a more flexible model of distirbuions will suffice. If not, GPs can easily be extended to more general Gaussian-mixture-priors.
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