54 citations · 91 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
In Search of Lost (Mixing) Time: Adaptive Markov chain Monte Carlo schemes for Bayesian variable selection with very large p
Jim Griffin, Krys Latuszynski, Mark Steel
The availability of data sets with large numbers of variables is rapidly increasing. The effective application of Bayesian variable selection methods for regression with these data…
Individual adaptation: an adaptive MCMC scheme for variable selection problems
Jim Griffin, Krzysztof Latuszynski, Mark Steel
The increasing size of data sets has lead to variable selection in regression becoming increasingly important. Bayesian approaches are attractive since they allow uncertainty about…
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…
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…