# Bayesian Deconditional Kernel Mean Embeddings

@article{Hsu2019BayesianDK, title={Bayesian Deconditional Kernel Mean Embeddings}, author={Kelvin Hsu and Fabio Tozeto Ramos}, journal={ArXiv}, year={2019}, volume={abs/1906.00199} }

Conditional kernel mean embeddings form an attractive nonparametric framework for representing conditional means of functions, describing the observation processes for many complex models. However, the recovery of the original underlying function of interest whose conditional mean was observed is a challenging inference task. We formalize deconditional kernel mean embeddings as a solution to this inverse problem, and show that it can be naturally viewed as a nonparametric Bayes' rule… Expand

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