Importance Weighting Approach in Kernel Bayes' Rule. (arXiv:2202.02474v3 [stat.ML] UPDATED)

We study a nonparametric approach to Bayesian computation via feature means,
where the expectation of prior features is updated to yield expected kernel
posterior features, based on regression from learned neural net or kernel
features of the observations. All quantities involved in the Bayesian update
are learned from observed data, making the method entirely model-free. The
resulting algorithm is a novel instance of a kernel Bayes' rule (KBR), based on
importance weighting. This results in superior numerical stability to the …

arxiv bayes importance kernel ml