26 citations · 55 across the 10 of their papers we have counts for
5 papers · 1 filter
Air Markov Chain Monte Carlo
Cyril Chimisov, Krzysztof Latuszynski, Gareth Roberts
We introduce a class of Adapted Increasingly Rarely Markov Chain Monte Carlo (AirMCMC) algorithms where the underlying Markov kernel is allowed to be changed based on the whole ava…
Adapting The Gibbs Sampler
Cyril Chimisov, Krzysztof Latuszynski, Gareth Roberts
The popularity of Adaptive MCMC has been fueled on the one hand by its success in applications, and on the other hand, by mathematically appealing and computationally straightforwa…
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…
Nonasymptotic bounds on the mean square error for MCMC estimates via renewal techniques
Krzysztof Latuszynski, Blazej Miasojedow, Wojciech Niemiro
The Nummellin's split chain construction allows to decompose a Markov chain Monte Carlo (MCMC) trajectory into i.i.d. "excursions". RegenerativeMCMC algorithms based on this techni…
Adaptive Gibbs samplers
Krzysztof Latuszynski, Jeffrey S. Rosenthal
We consider various versions of adaptive Gibbs and Metropolis within-Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on t…