Conditional Deep Inverse Rosenblatt Transports. (arXiv:2106.04170v2 [stat.ML] UPDATED)

Web: http://arxiv.org/abs/2106.04170

We present a novel offline-online method to mitigate the computational burden
of Bayesian inference, particularly in the regime where the posterior densities
are computationally demanding to evaluate while real-time inference results are
needed. In the offline phase, the proposed method learns the joint law of the
parameter random variables and the observable random variables in the
tensor-train (TT) format. Then, in the online phase, the resulting
order-preserving transport can be conditioned on newly observed data to
characterize the posterior random …

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