A hybrid VI-HMC method runs expensive Hamiltonian Monte Carlo only on the neural network parameters most responsible for predictive uncertainty, cutting sampling cost while approximating full HMC posteriors.
Graves, Practical variational inference for neural networks, Advances in neural in- formation processing systems 24 (2011)
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Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators
A hybrid VI-HMC method runs expensive Hamiltonian Monte Carlo only on the neural network parameters most responsible for predictive uncertainty, cutting sampling cost while approximating full HMC posteriors.