Joris Bierkens - Team

Joris Bierkens - Team

Current team members

Group meetings

2022
2021
Monday 6 DecArdjenStrong Invariance Principles for Ergodic Markov Processes. [link]
Friday 14 OctPaulEstimates, uniformly in time.
Thursday 30 SepJorisAkaike, H. (1974). A new look at the statistical model identification. [link]
Thursday 1 JulAndreaNagapetyan, T., Duncan, A. B., Hasenclever, L., Vollmer, S. J., Szpruch, L., & Zygalakis, K. (2017). The True Cost of Stochastic Gradient Langevin Dynamics. [link]
Friday 18 JunSebastianoRoberts, G. O., Rosenthal, J. S., & Tawn, N. G. (2020). Skew Brownian Motion and Complexity of the ALPS Algorithm. [link]
Friday 11 JunArdjenVan Der Vaart, A. W., & Van Zanten, J. H. (2008). Rates of contraction of posterior distributions based on Gaussian process priors. [link]
Thursday 20 MayPaulGarbuno-Inigo, A., Hoffmann, F., Li, W., & Stuart, A. M. (2020). Interacting Langevin diffusions: Gradient structure and ensemble Kalman sampler. [link]
Friday 30 AprilJoris Coghi, F., Chetrite, R., & Touchette, H. (2021). Role of current fluctuations in nonreversible samplers. [link]
Wednesday 14 AprAndreaCoupling of Euler approximations for PDMC. [link]
Friday 26 MarSebastianoNishimura, A., Dunson, D., & Lu, J. (2017). Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods. [link]
Thursday 11 MarArdjenChwialkowski, K., Strathmann, H., & Gretton, A. (2016). A kernel test of goodness of fit. [link]
Thursday 25 FebPaulN. Ross (2011). Fundamentals of Stein's method, continued. [link]
Thursday 11 FebJorisN. Ross (2011). Fundamentals of Stein's method, continued. [link]
Friday 29 JanAndreaN. Ross (2011). Fundamentals of Stein's method, continued. [link]
Friday 14 JanSebastianoN. Ross (2011). Fundamentals of Stein's method, continued. [link]
2020
Thursday 10 DecArdjenN. Ross (2011). Fundamentals of Stein's method. [link]
Friday 27 NovSjoerdSpectral analysis for the zigzag process on the torus.
Thursday 12 NovPaul Lu, J., & Wang, L. (2020). On explicit L^2-convergence rate estimate for piecewise deterministic Markov processes. [link]
Cao, Y., Lu, J., & Wang, L. (2019). On explicit L^2-convergence rate estimate for underdamped Langevin dynamics. [link]
Thursday 29 OctJorisO’Leary, J., Wang, G., & Jacob, P. E. (2020). Maximal couplings of the Metropolis-Hastings algorithm. [link]
Thursday 15 OctAndreaDurmus, A., & Moulines, É. (2019). High-dimensional Bayesian inference via the unadjusted Langevin algorithm. Bernoulli, 25(4 A), 2854–2882. [link]
Thursday 1 OctSebastianoSyed, S., Bouchard-Côté, A., Deligiannidis, G., & Doucet, A. (2019). Non-Reversible Parallel Tempering: a Scalable Highly Parallel MCMC Scheme. [link]
Thursday 10 SepArdjenChapters 1, 2 of Komorowski, T., Landim, C., & Olla, S. (2012). Fluctuations in Markov processes. Springer, Heidelberg. [link]
Duncan, A. B., Lelièvre, T., & Pavliotis, G. A. (2015). Variance Reduction using Nonreversible Langevin Samplers. Journal of Statistical Physics. [link]
Tuesday 23 JunePaulMattingly, 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 JunJorisGorham, 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 MayAndreaRosenthal, 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 MaySebastianoCotter, 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 AprArdjenHobert, 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 AprPaulBarré, J., Dobson, P., Ottobre, M., & Zatorska, E. (2020). Fast non mean-field networks: uniform in time averaging. [link]
Tuesday 31 MarJorisZig-Zag and BPS for anisotropic targets
Tuesday 10 MarAndreaJacob, 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 FebSebastianoRoberts, 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 JanJorisGuillin, 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 DecArdjenFlegal, 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 NovPaulAndrieu, 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 NovAndrea 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 OctSebastiano Andrieu, C., & Livingstone, S. (2019). Peskun-Tierney ordering for Markov chain and process Monte Carlo: beyond the reversible scenario, 1–38. [link]
Thursday 3 OctJorisMeet & Greet