Web: http://arxiv.org/abs/2209.10512

Sept. 22, 2022, 1:13 a.m. | Marius Millea

stat.ML updates on arXiv.org arxiv.org

We apply the technique of implicit differentiation to boost performance,
reduce numerical error, and remove required user-tuning in the Marginal
Unbiased Score Expansion (MUSE) algorithm for hierarchical Bayesian inference.
We demonstrate these improvements on three representative inference problems:
1) an extended Neal's funnel 2) Bayesian neural networks, and 3) probabilistic
principal component analysis. On our particular test cases, MUSE with implicit
differentiation is faster than Hamiltonian Monte Carlo by factors of 155, 397,
and 5, respectively, or factors of 65, …

arxiv differentiation unbiased

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