A transport approach to sequential simulation-based inference
Paul-Baptiste Rubio Youssef Marzouk Matthew Parno
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.