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Tag: priors

  • Universal priors: solving empirical Bayes via Bayesian inference and pretraining

    Universal priors: solving empirical Bayes via Bayesian inference and pretraining arXiv:2602.15136v1 Announce Type: new Abstract: We theoretically justify the recent empirical finding of [Teh et al., 2025] that a transformer pretrained on synthetically generated data achieves strong performance on empirical Bayes (EB) problems. We take an indirect approach to this question: rather than analyzing the…

  • Bayesian neural networks with interpretable priors from Mercer kernels

    Bayesian neural networks with interpretable priors from Mercer kernels arXiv:2510.23745v1 Announce Type: new Abstract: Quantifying the uncertainty in the output of a neural network is essential for deployment in scientific or engineering applications where decisions must be made under limited or noisy data. Bayesian neural networks (BNNs) provide a framework for this purpose by constructing…

  • Data-Driven Priors in the Maximum Entropy on the Mean Method for Linear Inverse Problems

    Data-Driven Priors in the Maximum Entropy on the Mean Method for Linear Inverse Problems arXiv:2412.17916v1 Announce Type: new Abstract: We establish the theoretical framework for implementing the maximumn entropy on the mean (MEM) method for linear inverse problems in the setting of approximate (data-driven) priors. We prove a.s. convergence for empirical means and further develop…