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Maximum Likelihood Learning of Unnormalized Models for Simulation-Based Inference

P Glaser, M Arbel, A Doucet, A Gretton - arXiv preprint arXiv:2210.14756, 2022 - arxiv.org
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We introduce two synthetic likelihood methods for Simulation-Based Inference (SBI), to conduct either amortized or targeted inference from experimental observations …

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@article{2210.14756v2,
Author        = {Pierre Glaser and Michael Arbel and Samo Hromadka and Arnaud Doucet and Arthur Gretton},
Title         = {Maximum Likelihood Learning of Unnormalized Models for Simulation-Based
  Inference},
Eprint        = {2210.14756v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {We introduce two synthetic likelihood methods for Simulation-Based Inference
(SBI), to conduct either amortized or targeted inference from experimental
observations when a high-fidelity simulator is available. Both methods learn a
conditional energy-based model (EBM) of the likelihood using synthetic data
generated by the simulator, conditioned on parameters drawn from a proposal
distribution. The learned likelihood can then be combined with any prior to
obtain a posterior estimate, from which samples can be drawn using MCMC. Our
methods uniquely combine a flexible Energy-Based Model and the minimization of
a KL loss: this is in contrast to other synthetic likelihood methods, which
either rely on normalizing flows, or minimize score-based objectives; choices
that come with known pitfalls. We demonstrate the properties of both methods on
a range of synthetic datasets, and apply them to a neuroscience model of the
pyloric network in the crab, where our method outperforms prior art for a
fraction of the simulation budget.},
Year          = {2022},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2210.14756v2},
File          = {2210.14756v2.pdf}
}

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