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Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks

Niall Jeffrey 1 Benjamin D. Wandelt 2
1 Astrophysique
LPENS (UMR_8023) - Laboratoire de physique de l'ENS - ENS Paris
Abstract : High-dimensional probability density estimation for inference suffers from the "curse of dimensionality". For many physical inference problems, the full posterior distribution is unwieldy and seldom used in practice. Instead, we propose direct estimation of lower-dimensional marginal distributions, bypassing high-dimensional density estimation or high-dimensional Markov chain Monte Carlo (MCMC) sampling. By evaluating the two-dimensional marginal posteriors we can unveil the full-dimensional parameter covariance structure. We additionally propose constructing a simple hierarchy of fast neural regression models, called Moment Networks, that compute increasing moments of any desired lower-dimensional marginal posterior density; these reproduce exact results from analytic posteriors and those obtained from Masked Autoregressive Flows. We demonstrate marginal posterior density estimation using high-dimensional LIGO-like gravitational wave time series and describe applications for problems of fundamental cosmology.
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Submitted on : Tuesday, December 8, 2020 - 9:30:13 PM
Last modification on : Thursday, July 1, 2021 - 5:33:19 PM

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Niall Jeffrey, Benjamin D. Wandelt. Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks. 34th Conference on Neural Information Processing Systems, Dec 2020, Online Conference, Canada. ⟨hal-03047530⟩



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