Simulation-based inference using surjective sequential neural likelihood estimation

• URL: http://arxiv.org/abs/2308.01054v2
• Date: Fri, 23 Feb 2024 07:39:14 GMT
• Title: Simulation-based inference using surjective sequential neural likelihood estimation
• Authors: Simon Dirmeier, Carlo Albert, Fernando Perez-Cruz
• Abstract summary: Surjective Sequential Neural Likelihood estimation is a novel method for simulation-based inference. By embedding the data in a low-dimensional space, SSNL solves several issues previous likelihood-based methods had when applied to high-dimensional data sets.
• Score: 50.24983453990065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Surjective Sequential Neural Likelihood (SSNL) estimation, a novel method for simulation-based inference in models where the evaluation of the likelihood function is not tractable and only a simulator that can generate synthetic data is available. SSNL fits a dimensionality-reducing surjective normalizing flow model and uses it as a surrogate likelihood function which allows for conventional Bayesian inference using either Markov chain Monte Carlo methods or variational inference. By embedding the data in a low-dimensional space, SSNL solves several issues previous likelihood-based methods had when applied to high-dimensional data sets that, for instance, contain non-informative data dimensions or lie along a lower-dimensional manifold. We evaluate SSNL on a wide variety of experiments and show that it generally outperforms contemporary methods used in simulation-based inference, for instance, on a challenging real-world example from astrophysics which models the magnetic field strength of the sun using a solar dynamo model.

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