3 citations · 4 across the 4 of their papers we have counts for
4 papers
Variational inference for count response semiparametric regression
Jan Luts, Matt P. Wand
Fast variational approximate algorithms are developed for Bayesian semiparametric regression when the response variable is a count, i.e. a non-negative integer. We treat both the P…
Real-time semiparametric regression for distributed data sets
Jan Luts
This paper proposes a method for semiparametric regression analysis of large-scale data which are distributed over multiple hosts. This enables modeling of nonlinear relationships…
Mean field variational Bayesian inference for support vector machine classification
Jan Luts, John T. Ormerod
A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented. This representation allows…
Real-time semiparametric regression
Jan Luts, Tamara Broderick, Matt P. Wand
We develop algorithms for performing semiparametric regression analysis in real time, with data processed as it is collected and made immediately available via modern telecommunica…