A transport approach to sequential simulation-based inference

• URL: http://arxiv.org/abs/2308.13940v1
• Date: Sat, 26 Aug 2023 18:53:48 GMT
• Title: A transport approach to sequential simulation-based inference
• Authors: Paul-Baptiste Rubio and Youssef Marzouk and Matthew Parno
• Abstract summary: We present a new transport-based approach to efficiently perform sequential Bayesian inference of static model parameters. The strategy is based on the extraction of conditional distribution from the joint distribution of parameters and data, via the estimation of structured (e.g., block triangular) transport maps. This allow gradient-based characterizations of posterior density via transport maps in a model-free, online phase.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a new transport-based approach to efficiently perform sequential Bayesian inference of static model parameters. The strategy is based on the extraction of conditional distribution from the joint distribution of parameters and data, via the estimation of structured (e.g., block triangular) transport maps. This gives explicit surrogate models for the likelihood functions and their gradients. This allow gradient-based characterizations of posterior density via transport maps in a model-free, online phase. This framework is well suited for parameter estimation in case of complex noise models including nuisance parameters and when the forward model is only known as a black box. The numerical application of this method is performed in the context of characterization of ice thickness with conductivity measurements.

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