6 citations · 6 across the 3 of their papers we have counts for
4 papers
Sparse Horseshoe Estimation via Expectation-Maximisation
Shu Yu Tew, Daniel F. Schmidt, Enes Makalic
The horseshoe prior is known to possess many desirable properties for Bayesian estimation of sparse parameter vectors, yet its density function lacks an analytic form. As such, it…
Introduction to minimum message length inference
Enes Makalic, Daniel F. Schmidt
The aim of this manuscript is to introduce the Bayesian minimum message length principle of inductive inference to a general statistical audience that may not be familiar with info…
A Minimum Message Length Criterion for Robust Linear Regression
Chi Kuen Wong, Enes Makalic, Daniel F. Schmidt
This paper applies the minimum message length principle to inference of linear regression models with Student-t errors. A new criterion for variable selection and parameter estimat…
Bayesian Sparse Global-Local Shrinkage Regression for Selection of Grouped Variables
Zemei Xu, Daniel F. Schmidt, Enes Makalic +2
Most estimates for penalised linear regression can be viewed as posterior modes for an appropriate choice of prior distribution. Bayesian shrinkage methods, particularly the horses…