• Corpus ID: 249375545

Mean field approximations via log-concavity

@inproceedings{Lacker2022MeanFA,
  title={Mean field approximations via log-concavity},
  author={Daniel Lacker and Sumit Mukherjee and Lane Chun Yeung},
  year={2022}
}
We propose a new approach to deriving quantitative mean field approximations for any probability measure P on R with density proportional to e, for f strongly concave. We bound the mean field approximation for the log partition function log ∫ edx in terms of ∑ i6=j EQ∗ |∂ijf | , for a semi-explicit probability measure Q characterized as the unique mean field optimizer, or equivalently as the minimizer of the relative entropy H(· |P ) over product measures. This notably does not involve metric… 
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