9 citations · 24 across the 11 of their papers we have counts for
4 papers · 1 filter
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…
Methods and Tools for Bayesian Variable Selection and Model Averaging in Univariate Linear Regression
Anabel Forte, Gonzalo Garcia-Donato, Mark Steel
In this paper we briefly review the main methodological aspects concerned with the application of the Bayesian approach to model choice and model averaging in the context of variab…
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…
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…