| 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 | 240 | 2015 |
| A framework for adaptive MCMC targeting multimodal distributions E Pompe, C Holmes, K Łatuszyński The Annals of Statistics 48 (5), 2930-2952, 2020 | 96* | 2020 |
| Adaptive Gibbs samplers and related MCMC methods K Latuszynski, GO Roberts, JS Rosenthal The Annals of Applied Probability, 2013, 2013 | 80* | 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 | 62 | 2014 |
| Nonasymptotic bounds on the estimation error of MCMC algorithms K Łatuszyński, B Miasojedow, W Niemiro Bernoulli 19 (5A), 2033-2066, 2013 | 62 | 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 | 52* | 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 | 50 | 2011 |
| Rigorous confidence bounds for MCMC under a geometric drift condition K Łatuszyński, W Niemiro Journal of Complexity 27 (1), 23-38, 2011 | 36* | 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 | 34 | 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 | 30 | 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 | 28 | 2015 |
| CLTs and asymptotic variance of time-sampled Markov chains K Łatuszyński, GO Roberts Methodology and Computing in Applied Probability, 1-11, 2011 | 28 | 2011 |
| 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 | 23* | 2023 |
| Convergence of hybrid slice sampling via spectral gap K Łatuszyński, D Rudolf Advances in Applied Probability 56 (4), 1440-1466, 2024 | 20* | 2024 |
| Perfect simulation using atomic regeneration with application to sequential Monte Carlo A Lee, A Doucet, K Łatuszyński arXiv preprint arXiv:1407.5770, 2014 | 20 | 2014 |
| Optimal scaling of MCMC beyond Metropolis S Agrawal, D Vats, K Łatuszyński, GO Roberts Advances in Applied Probability 55 (2), 492-509, 2023 | 17 | 2023 |
| Efficient Bernoulli factory Markov chain Monte Carlo for intractable posteriors D Vats, FB Gonçalves, K Łatuszyński, GO Roberts Biometrika 109 (2), 369-385, 2022 | 17* | 2022 |
| 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 | 17 | 2017 |
| Adapting the Gibbs sampler C Chimisov, K Latuszynski, G Roberts arXiv preprint arXiv:1801.09299, 2018 | 16 | 2018 |
| Barker’s algorithm for Bayesian inference with intractable likelihoods FB Gonçalves, K Latuszynski, GO Roberts Brazilian Journal of Probability and Statistics, 2017 | 16 | 2017 |
Gareth RobertsProfessor of Statistics, University of Warwickwarwick.ac.uk의 이메일 확인됨
