BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Henrik Häggström (Chalmers University of Technology & University of Gothenburg) DTSTART:20250122T121500Z DTEND:20250122T130000Z DTSTAMP:20260422T155025Z UID:gbgstats/76 DESCRIPTION:Title: Simulation-based inference for stochastic nonlinear mixed-effects models with applications in systems biology\nby Henrik Häggström (Chalmers University of Technology & University of Gothenburg) as part of Gothenbur g statistics seminar\n\nLecture held in MVL14.\n\nAbstract\nWe propose a n ovel methodology for Bayesian inference in hierarchical mixed-effects mode ls. We construct a framework that is highly scalable\, where amortized app roximations to the likelihood and the parameters posterior are first obtai ned\, and these are rapidly refined for each individual dataset\, to ultim ately approximate the parameters posterior across many individuals. The fr amework introduced is easily trainable\, as it uses mixture of experts but without neural networks\, leading to parsimonious yet expressive surrogat e models of the likelihood and the posterior. The methodology is exemplifi ed via stochastic differential equation mixed-effects models\, that are hi ghly relevant in systems biology\, but the methodology is general and can accommodate other types of stochastic and deterministic models. We compare our approximate inference with exact pseudomarginal inference and show th at our methodology is fast and competitive.\n LOCATION:https://researchseminars.org/talk/gbgstats/76/ END:VEVENT END:VCALENDAR