45 citations · 150 across the 12 of their papers we have counts for
12 papers
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…
Exact Bayesian inference for diffusion driven Cox processes
Flavio B. Gonçalves, Krzysztof G. Łatuszyński, Gareth O. Roberts
In this paper, we present a novel methodology to perform Bayesian inference for Cox processes in which the intensity function is driven by a diffusion process. The novelty lies in…
Exact Monte Carlo likelihood-based inference for jump-diffusion processes
Flávio B. Gonçalves, Krzysztof G. Łatuszyński, Gareth O. Roberts
Statistical inference for discretely observed jump-diffusion processes is a complex problem which motivates new methodological challenges. Thus existing approaches invariably resor…
Convergence of hybrid slice sampling via spectral gap
Krzysztof Łatuszyński, Daniel Rudolf
It is known that the simple slice sampler has robust convergence properties, however the class of problems where it can be implemented is limited. In contrast, we consider hybrid s…
Stability of adversarial Markov chains, with an application to adaptive MCMC algorithms
Radu V. Craiu, Lawrence Gray, Krzysztof Łatuszyński +3
We consider whether ergodic Markov chains with bounded step size remain bounded in probability when their transitions are modified by an adversary on a bounded subset. We provide c…
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…
Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation
Anthony Lee, Krzysztof Latuszynski
Approximate Bayesian computation has emerged as a standard computational tool when dealing with the increasingly common scenario of completely intractable likelihood functions in B…
Nonasymptotic bounds on the estimation error of MCMC algorithms
Krzysztof Łatuszyński, Błażej Miasojedow, Wojciech Niemiro
We address the problem of upper bounding the mean square error of MCMC estimators. Our analysis is nonasymptotic. We first establish a general result valid for essentially all ergo…
CLTs and asymptotic variance of time-sampled Markov chains
Krzysztof Latuszynski, Gareth O. Roberts
For a Markov transition kernel and a probability distribution on nonnegative integers, a time-sampled Markov chain evolves according to the transition kernel $P_μ = \sum_k…
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…