PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling

• URL: http://arxiv.org/abs/2304.04307v3
• Date: Fri, 10 Nov 2023 13:22:01 GMT
• Title: PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling
• Authors: Elizaveta Semenova, Prakhar Verma, Max Cairney-Leeming, Arno Solin, Samir Bhatt, Seth Flaxman
• Abstract summary: Recent have shown that GP priors can be encoded using deep generative models such as variational autoencoders (VAEs) We show how VAEs can serve as drop-in replacements for the original priors during MCMC inference. We propose PriorCVAE to encode solutions of ODEs.
• Score: 12.820453440015553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances have shown that GP priors, or their finite realisations, can be encoded using deep generative models such as variational autoencoders (VAEs). These learned generators can serve as drop-in replacements for the original priors during MCMC inference. While this approach enables efficient inference, it loses information about the hyperparameters of the original models, and consequently makes inference over hyperparameters impossible and the learned priors indistinct. To overcome this limitation, we condition the VAE on stochastic process hyperparameters. This allows the joint encoding of hyperparameters with GP realizations and their subsequent estimation during inference. Further, we demonstrate that our proposed method, PriorCVAE, is agnostic to the nature of the models which it approximates, and can be used, for instance, to encode solutions of ODEs. It provides a practical tool for approximate inference and shows potential in real-life spatial and spatiotemporal applications.

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