54 citations · 91 across the 16 of their papers we have counts for
9 papers · 1 filter
Adaptive MCMC for Bayesian variable selection in generalised linear models and survival models
Xitong Liang, Samuel Livingstone, Jim Griffin
Developing an efficient computational scheme for high-dimensional Bayesian variable selection in generalised linear models and survival models has always been a challenging problem…
Bayesian inference of vector autoregressions with tensor decompositions
Yiyong Luo, Jim E. Griffin
Vector autoregressions (VARs) are popular model for analyzing multivariate economic time series. However, VARs can be over-parameterized if the numbers of variables and lags are mo…
Normalized Latent Measure Factor Models
Mario Beraha, Jim E. Griffin
We propose a methodology for modeling and comparing probability distributions within a Bayesian nonparametric framework. Building on dependent normalized random measures, we consid…
A loss discounting framework for model averaging and selection in time series models
Dawid Bernaciak, Jim E. Griffin
We introduce a Loss Discounting Framework for model and forecast combination which generalises and combines Bayesian model synthesis and generalized Bayes methodologies. We use a l…
Beta-CoRM: A Bayesian Approach for -gram Profiles Analysis
José A. Perusquía, Jim E. Griffin, Cristiano Villa
-gram profiles have been successfully and widely used to analyse long sequences of potentially differing lengths for clustering or classification. Mainly, machine learning algor…
Modelling and computation using NCoRM mixtures for density regression
Jim Griffin, Fabrizio Leisen
Normalized compound random measures are flexible nonparametric priors for related distributions. We consider building general nonparametric regression models using normalized compo…