45 citations · 150 across the 12 of their papers we have counts for
5 papers · 1 filter
Stereographic Markov Chain Monte Carlo
Jun Yang, Krzysztof Łatuszyński, Gareth O. Roberts
High-dimensional distributions, especially those with heavy tails, are notoriously difficult for off-the-shelf MCMC samplers: the combination of unbounded state spaces, diminishing…
Optimal Scaling of MCMC Beyond Metropolis
Sanket Agrawal, Dootika Vats, Krzysztof Łatuszyński +1
The problem of optimally scaling the proposal distribution in a Markov chain Monte Carlo algorithm is critical to the quality of the generated samples. Much work has gone into obta…
The Containment Condition and AdapFail algorithms
Krzysztof Latuszynski, Jeffrey S. Rosenthal
This short note investigates convergence of adaptive MCMC algorithms, i.e.\ algorithms which modify the Markov chain update probabilities on the fly. We focus on the Containment co…
Adaptive Gibbs samplers and related MCMC methods
Krzysztof Łatuszyński, Gareth O. Roberts, 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…
Simulating Events of Unknown Probabilities via Reverse Time Martingales
Krzysztof Latuszynski, Ioannis Kosmidis, Omiros Papaspiliopoulos +1
Assume that one aims to simulate an event of unknown probability which is uniquely determined, however only its approximations can be obtained using a finite computati…