Andrew R. Barron

Rapid Bayesian computation and estimation for neural networks via log-concave coupling

It is demonstrated that the posterior density can be written as a mixture density with suitably defined auxiliary random variables, where the mixture components are log-concave, and neuron parameters can be sampled from the posterior by only sampling log-concave densities.