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October 7, 2025Open Access

Near-Optimal Approximations for Bayesian Inference in Function Space

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The algorithm approximates Bayes posteriors via RKHS-valued Langevin diffusion, showing scalability and efficiency.
It achieves a computational complexity of O(M^3 + JM^2), making it feasible for large-scale problems.
The method, based on a non-parametric variational family, connects closely to the sparse variational Gaussian process model.
It demonstrates provable proximity to optimal M-dimensional variations of the Bayes posterior under specific likelihood assumptions.

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This algorithm approximates Bayes posteriors in reproducing kernel Hilbert space, suggesting efficiency in inference and scalability.

Wild et al. (2025) studied this question.

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