Krzysztof Latuszynski
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Bayesian computation: a summary of the current state, and samples backwards and forwards
PJ Green, K Łatuszyński, M Pereyra, CP Robert
Statistics and Computing 25, 835-862, 2015
A framework for adaptive MCMC targeting multimodal distributions
E Pompe, C Holmes, K Łatuszyński
The Annals of Statistics 48 (5), 2930-2952, 2020
Adaptive Gibbs samplers and related MCMC methods
K Latuszynski, GO Roberts, JS Rosenthal
The Annals of Applied Probability, 2013, 2013
Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation
A Lee, K Latuszynski
Biometrika 101 (3), 655--671, 2014
Nonasymptotic bounds on the estimation error of MCMC algorithms
K Łatuszyński, B Miasojedow, W Niemiro
Bernoulli 19 (5A), 2033-2066, 2013
In search of lost mixing time: adaptive Markov chain Monte Carlo schemes for Bayesian variable selection with very large p
JE Griffin, KG Łatuszyński, MFJ Steel
Biometrika 108 (1), 53-69, 2021
Simulating events of unknown probabilities via reverse time martingales
K Łatuszyński, I Kosmidis, O Papaspiliopoulos, GO Roberts
Random Structures & Algorithms 38 (4), 441-452, 2011
Rigorous confidence bounds for MCMC under a geometric drift condition
K Łatuszyński, W Niemiro
Journal of Complexity 27 (1), 23-38, 2011
A few remarks on “Fixed-width output analysis for Markov chain Monte Carlo” by Jones et al
W Bednorz, K Latuszynski
Journal of the American Statistical Association 102 (480), 1485-1486, 2007
A Regeneration Proof of the Central Limit Theorem for Uniformly Ergodic Markov Chains
W Bednorz, K Latuszynski, R Latala
Elect. Comm. in Probab 13, 85-98, 2008
Stability of Adversarial Markov Chains, with an Application to Adaptive MCMC Algorithms
RV Craiu, L Gray, K Latuszynski, N Madras, GO Roberts, JS Rosenthal
The Annals of Applied Probability 25 (6), 3592–3623, 2015
CLTs and asymptotic variance of time-sampled Markov chains
K Łatuszyński, GO Roberts
Methodology and Computing in Applied Probability, 1-11, 2011
Perfect simulation using atomic regeneration with application to sequential Monte Carlo
A Lee, A Doucet, K Łatuszyński
arXiv preprint arXiv:1407.5770, 2014
Exact Monte Carlo likelihood-based inference for jump-diffusion processes
FB Gonçalves, KG Łatuszyński, GO Roberts
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2023
Continious-time importance sampling: Monte Carlo methods which avoid time-discretisation error
P Fearnhead, K Latuszynski, GO Roberts, G Sermaidis
arXiv preprint arXiv:1712.06201, 2017
Convergence of hybrid slice sampling via spectral gap
K Łatuszyński, D Rudolf
arXiv preprint arXiv:1409.2709, 2014
Barker’s algorithm for Bayesian inference with intractable likelihoods
FB Gonçalves, K Latuszynski, GO Roberts
Brazilian Journal of Probability and Statistics, 2017
The containment condition and AdapFail algorithms
K Łatuszyński, JS Rosenthal
Journal of Applied Probability, 2014
Optimal scaling of MCMC beyond Metropolis
S Agrawal, D Vats, K Łatuszyński, GO Roberts
Advances in Applied Probability 55 (2), 492-509, 2023
Adapting the Gibbs sampler
C Chimisov, K Latuszynski, G Roberts
arXiv preprint arXiv:1801.09299, 2018
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