Neural Surrogate HMC: Accelerated Hamiltonian Monte Carlo with a Neural Network Surrogate Likelihood
<|reference_start|>Neural Surrogate HMC: Accelerated Hamiltonian Monte Carlo with a Neural Network Surrogate Likelihood: Bayesian Inference with Markov Chain Monte Carlo requires efficient computation of the likelihood function. In some scientific applications, the likelihood must be computed by numerically solving a partial differential equation, which can be prohibitively expensive. We demonstrate that some such problems can be made tractable by amortizing the computation with a surrogate likelihood function implemented by a neural network. We show that this has two additional benefits: reducing noise in the likelihood evaluations and providing fast gradient calculations. In experiments, the approach is applied to a model of heliospheric transport of galactic cosmic rays, where it enables efficient sampling from the posterior of latent parameters in the Parker equation.<|reference_end|>
@article{wolniewicz2024neural,
    title={Neural Surrogate HMC: Accelerated Hamiltonian Monte Carlo with a Neural
  Network Surrogate Likelihood},
    author={Linnea M Wolniewicz, Peter Sadowski, Claudio Corti},
    journal={arXiv preprint arXiv:2407.20432},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20432},
    primaryClass={cs.LG astro-ph.HE}
}