DiME estimates model evidence for diffusion priors by integrating time-marginals from posterior sampling, enabling efficient prior selection and misfit diagnosis in ill-posed inverse problems.
UnlikeΘ high, the variance of this estimator goes to0asσ t →0, but for the rest of diffusion process the constant factor makes it a higher variance estimator thanΘ high in practice
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Sample-efficient evidence estimation of score based priors for model selection
DiME estimates model evidence for diffusion priors by integrating time-marginals from posterior sampling, enabling efficient prior selection and misfit diagnosis in ill-posed inverse problems.