54 citations · 91 across the 16 of their papers we have counts for
4 papers · 1 filter
An adaptive truncation method for inference in Bayesian nonparametric models
Jim E. Griffin
Many exact Markov chain Monte Carlo algorithms have been developed for posterior inference in Bayesian nonparametric models which involve infinite-dimensional priors. However, thes…
Hierarchical sparsity priors for regression models
Jim E. Griffin, Philip J. Brown
We focus on the increasingly important area of sparse regression problems where there are many variables and the effects of a large subset of these are negligible. This paper descr…
Adaptive MC^3 and Gibbs algorithms for Bayesian Model Averaging in Linear Regression Models
Demetris Lamnisos, Jim E. Griffin, Mark F. J. Steel
The MC (Madigan and York, 1995) and Gibbs (George and McCulloch, 1997) samplers are the most widely implemented algorithms for Bayesian Model Averaging (BMA) in linear regressi…
Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data
Richard S. Savage, Zoubin Ghahramani, Jim E. Griffin +2
We present a nonparametric Bayesian method for disease subtype discovery in multi-dimensional cancer data. Our method can simultaneously analyse a wide range of data types, allowin…