no code implementations • 1 Jun 2019 • Kelvin Hsu, Fabio Ramos
Conditional kernel mean embeddings form an attractive nonparametric framework for representing conditional means of functions, describing the observation processes for many complex models.
no code implementations • 3 Mar 2019 • Kelvin Hsu, Fabio Ramos
In likelihood-free settings where likelihood evaluations are intractable, approximate Bayesian computation (ABC) addresses the formidable inference task to discover plausible parameters of simulation programs that explain the observations.
1 code implementation • 1 Sep 2018 • Kelvin Hsu, Richard Nock, Fabio Ramos
Conditional kernel mean embeddings are nonparametric models that encode conditional expectations in a reproducing kernel Hilbert space.