JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models

• URL: http://arxiv.org/abs/2302.09125v3
• Date: Tue, 20 Jun 2023 09:51:14 GMT
• Title: JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models
• Authors: Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich K\"othe, Paul-Christian B\"urkner
• Abstract summary: We propose jointly amortized neural approximation'' (JANA) of intractable likelihood functions and posterior densities. We benchmark the fidelity of JANA on a variety of simulation models against state-of-the-art Bayesian methods.
• Score: 0.5872014229110214
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This work proposes ``jointly amortized neural approximation'' (JANA) of intractable likelihood functions and posterior densities arising in Bayesian surrogate modeling and simulation-based inference. We train three complementary networks in an end-to-end fashion: 1) a summary network to compress individual data points, sets, or time series into informative embedding vectors; 2) a posterior network to learn an amortized approximate posterior; and 3) a likelihood network to learn an amortized approximate likelihood. Their interaction opens a new route to amortized marginal likelihood and posterior predictive estimation -- two important ingredients of Bayesian workflows that are often too expensive for standard methods. We benchmark the fidelity of JANA on a variety of simulation models against state-of-the-art Bayesian methods and propose a powerful and interpretable diagnostic for joint calibration. In addition, we investigate the ability of recurrent likelihood networks to emulate complex time series models without resorting to hand-crafted summary statistics.

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