A new ABC algorithm associates summary statistics with energies and temperatures and derives an optimal annealing schedule from minimal entropy production on a Riemannian manifold of state variables.
arXiv preprint , year =
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Pre-trained TabPFN acts as an effective training-free summary network for neural posterior estimation, matching or outperforming standard methods while preserving useful marginal and location information in the posterior.
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A thermodynamic approach to Approximate Bayesian Computation with multiple summary statistics
A new ABC algorithm associates summary statistics with energies and temperatures and derives an optimal annealing schedule from minimal entropy production on a Riemannian manifold of state variables.
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Pre-trained Tabular Foundation Models as Versatile Summary Networks for Neural Posterior Estimation
Pre-trained TabPFN acts as an effective training-free summary network for neural posterior estimation, matching or outperforming standard methods while preserving useful marginal and location information in the posterior.