54 citations · 91 across the 16 of their papers we have counts for
17 papers
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…
eDNAPlus: A unifying modelling framework for DNA-based biodiversity monitoring
Alex Diana, Eleni Matechou, Jim Griffin +5
DNA-based biodiversity surveys involve collecting physical samples from survey sites and assaying the contents in the laboratory to detect species via their diagnostic DNA sequence…
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…
Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable Selection
Xitong Liang, Samuel Livingstone, Jim Griffin
We introduce a framework for efficient Markov Chain Monte Carlo (MCMC) algorithms targeting discrete-valued high-dimensional distributions, such as posterior distributions in Bayes…