BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Ruben Seyer (Chalmers University of Technology & University of Got henburg) DTSTART:20250521T111500Z DTEND:20250521T120000Z DTSTAMP:20260422T155025Z UID:gbgstats/90 DESCRIPTION:Title: Creating non-reversible rejection-free samplers by rebalancing skew-bala nced Markov jump processes\nby Ruben Seyer (Chalmers University of Tec hnology & University of Gothenburg) as part of Gothenburg statistics semin ar\n\nLecture held in MVL14.\n\nAbstract\nMarkov chain sampling methods fo rm the backbone of modern computational statistics. However\, many popular methods are prone to random walk behavior\, i.e.\, diffusion-like explora tion of the sample space\, leading to slow mixing that requires intricate tuning to alleviate. Non-reversible samplers can resolve some of these iss ues. We introduce a device that turns jump processes that satisfy a skew-d etailed balance condition for a reference measure into a process that samp les a target measure that is absolutely continuous with respect to the ref erence measure. The resulting sampler is rejection-free\, non-reversible\, and continuous-time. As an example\, we apply the device to Hamiltonian d ynamics discretized by the leapfrog integrator\, resulting in a rejection- free non-reversible continuous-time version of Hamiltonian Monte Carlo (HM C). We prove the geometric ergodicity of the resulting sampler under certa in convexity conditions\, and demonstrate its qualitatively different beha vior to HMC through numerical examples.\n LOCATION:https://researchseminars.org/talk/gbgstats/90/ END:VEVENT END:VCALENDAR