1 citations · 1 across the 5 of their papers we have counts for
4 papers · 1 filter
The Bernstein-von Mises theorem for Semiparametric Mixtures
Stefan Franssen, Jeanne Nguyen, Aad van der Vaart
Semiparametric mixture models are parametric models with latent variables. They are defined kernel, , where z is the unknown latent variable, and is the parameter o…
Empirical and Full Bayes estimation of the type of a Pitman-Yor process
S. E. M. P. Franssen, A. W. van der Vaart
The Pitman-Yor process is a random discrete probability distribution of which the atoms can be used to model the relative abundance of species. The process is indexed by a type par…
Uncertainty Quantification for nonparametric regression using Empirical Bayesian neural networks
Stefan Franssen, Botond Szabó
We propose a new, two-step empirical Bayes-type of approach for neural networks. We show in context of the nonparametric regression model that the procedure (up to a logarithmic fa…
The Bernstein-von Mises theorem for the Pitman-Yor process of nonnegative type
S. E. M. P. Franssen, A. W. van der Vaart
The Pitman-Yor process is a random probability distribution, that can be used as a prior distribution in a nonparametric Bayesian analysis. The process is of species sampling type…