BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Neil Chada (King Abdullah University of Science and Technology) DTSTART:20210706T140000Z DTEND:20210706T150000Z DTSTAMP:20260423T034448Z UID:DSCSS/23 DESCRIPTION:Title: U nbiased Inference for Discretely observed Hidden Markov Model Diffusions\nby Neil Chada (King Abdullah University of Science and Technology) as part of Data Science and Computational Statistics Seminar\n\n\nAbstract\nW e develop a Bayesian inference method for diffusions observed discretely a nd with noise\, which is free of discretisation bias. Unlike existing unbi ased inference methods\, our method does not rely on exact simulation tech niques. Instead\, our method uses standard time-discretised approximations of diffusions\, such as the Euler—Maruyama scheme. Our approach is base d on particle marginal Metropolis—Hastings\, a particle filter\, randomi sed multilevel Monte Carlo\, and importance sampling type correction of ap proximate Markov chain Monte Carlo. The resulting estimator leads to infer ence without a bias from the time-discretisation as the number of Markov c hain iterations increases. We give convergence results and recommend alloc ations for algorithm inputs. Our method admits a straightforward paralleli sation\, and can be computationally efficient. The user-friendly approach is illustrated in three examples\, where the underlying diffusion is an Or nstein—Uhlenbeck process\, a geometric Brownian motion\, and a 2d non-re versible Langevin equation.\n LOCATION:https://researchseminars.org/talk/DSCSS/23/ END:VEVENT END:VCALENDAR