| 2020 | ||
| Tuesday 23 June | Paul | Mattingly, J. C., Stuart, A. M., & Tretyakov, M. V. (2010). Convergence of Numerical Time-Averaging and Stationary Measures via Poisson Equations. SIAM Journal on Numerical Analysis, 48(2), 552–577. [link] |
| Tuesday 9 June | Joris | Gorham, J., & Mackey, L. (2015). Measuring Sample Quality with Stein’s Method. Advances in Neural Information Processing Systems 28, 226–234. [link] Gorham, J., Duncan, A. B., Vollmer, S. J., & Mackey, L. (2019). Measuring sample quality with diffusions. Ann. Appl. Probab., 29(5), 2884–2928. [link] [notes] |
| Tuesday 26 May | Andrea | Rosenthal, J. S. (2003). Asymptotic variance and convergence rates of nearly-periodic Markov chain Monte Carlo algorithms. Journal of the American Statistical Association, 98(461), 169–177. [link] |
| Tuesday 12 May | Sebastiano | Cotter, S. L., Roberts, G. O., Stuart, A. M., & White, D. (2013). MCMC Methods for Functions: Modifying Old Algorithms to Make Them Faster. Statistical Science, 28(3), 424–446. [link] |
| Tuesday 28 April | Ardjen | Hobert, J. P., Jones, G. L., Presnell, B., & Rosenthal, J. S. (2002). On the Applicability of Regenerative Simulation in Markov Chain Monte Carlo. Biometrika, 89(4), 731–743. [link] Löcherbach, E., & Loukianova, D. (2008). On Nummelin splitting for continuous time Harris recurrent Markov processes and application to kernel estimation for multi-dimensional diffusions. Stochastic Processes and Their Applications, 118(8), 1301–1321. [link] |
| Tuesday 15 April | Paul | Barré, J., Dobson, P., Ottobre, M., & Zatorska, E. (2020). Fast non mean-field networks: uniform in time averaging. [link] |
| Tuesday 31 March | Joris | Zig-Zag and BPS for anisotropic targets |
| Tuesday 10 March | Andrea | Jacob, P. E., O’Leary, J., & Atchadé, Y. F. (2020). Unbiased Markov chain Monte Carlo with couplings. J. R. Stat. Soc. B, 82(2), 1–32. [link] |
| Tuesday 18 February | Sebastiano | Roberts, G. O., Gelman, A., & Gilks, W. R. (1997). Weak convergence and optimal scaling of random walk Metropolis algorithms. The Annals of Applied Probability, 7(1), 110–120. [link] Roberts, G. O., & Rosenthal, J. S. (1998). Optimal scaling of discrete approximations to Langevin diffusions. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 60(1), 255–268. [link] Roberts, G. O., & Rosenthal, J. S. (2001). Optimal scaling for various Metropolis-Hastings algorithms. Statistical Science, 16(4), 351–367. [link] |
| Tuesday 21 January | Joris | Guillin, A., & Nectoux, B. (2020). Low lying eigenvalues and convergence to the equilibrium of some Piecewise Deterministic Markov Processes generators in the small temperature regime. [link] |
| 2019 | ||
| Tuesday 17 December | Ardjen | Flegal, J. M., & Jones, G. L. (2010). Batch means and spectral variance estimators in Markov chain Monte Carlo. The Annals of Statistics, 38(2), 1034–1070. |
| Friday 29 November | Paul | Andrieu, C., Durmus, A., Nüsken, N., & Roussel, J. (2018). Hypocoercivity of Piecewise Deterministic Markov Process-Monte Carlo. ArXiv Preprint ArXiv: 1808.08592. [link] Dolbeault, J., Mouhot, C., & Schmeiser, C. (2015). Hypocoercivity for linear kinetic equations conserving mass. Transactions of the American Mathematical Society, 367(6), 3807–3828. [link] |
| Friday 1 November | Andrea | Roberts, G. O., & Rosenthal, J. S. (2007). Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms. Journal of Applied Probability, 44(02), 458–475. [link] |
| Thursday 17 October | Sebastiano | Andrieu, C., & Livingstone, S. (2019). Peskun-Tierney ordering for Markov chain and process Monte Carlo: beyond the reversible scenario, 1–38. [link] |
| Thursday 3 October | Joris | Meet & Greet |