Reversible Gromov-Monge Sampler for Simulation-Based Inference. (arXiv:2109.14090v3 [stat.ME] UPDATED)

This paper introduces a new simulation-based inference procedure to model and
sample from multi-dimensional probability distributions given access to i.i.d.\
samples, circumventing the usual approaches of explicitly modeling the density
function or designing Markov chain Monte Carlo. Motivated by the seminal work
on distance and isomorphism between metric measure spaces, we propose a new
notion called the Reversible Gromov-Monge (RGM) distance and study how RGM can
be used to design new transform samplers to perform simulation-based inference.
Our RGM sampler …

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