BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CREST - ECPv5.1.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:CREST
X-ORIGINAL-URL:https://crest.science
X-WR-CALDESC:Events for CREST
BEGIN:VTIMEZONE
TZID:Europe/Helsinki
BEGIN:DAYLIGHT
TZOFFSETFROM:+0200
TZOFFSETTO:+0300
TZNAME:EEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
TZNAME:EET
DTSTART:20261025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20260914T140000
DTEND;TZID=Europe/Helsinki:20260914T150000
DTSTAMP:20260909T111001
CREATED:20260909T082841Z
LAST-MODIFIED:20260909T082841Z
UID:19120-1789394400-1789398000@crest.science
SUMMARY:Richard NICKL (University of Cambridge) - Statistical Inference for infinite-dimensional dynamical systems
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:00 pm\nDate: 14th September\nPlace: 3001 \n  \nRichard NICKL (University of Cambridge) – Statistical Inference for infinite-dimensional dynamical systems \n  \n Abstract:  \nWe study optimal statistical inference procedures for the states of time evolution phenomena occurring in `data assimilation’ or filtering problems. There it is a common practice to assign a Gaussian process prior on the initial condition of a dynamical system and to update it to a Bayesian posterior measure in the space of possible trajectories given a discrete sample of the process. In key applications the dynamics are non-linear\, such as with Navier-Stokes equations in geophysical sciences or reaction-diffusion equations in biochemistry. While Bayesian posterior distributions are widely computed by filtering or MCMC methods\, little is known about the statistical behaviour of these posterior measures in non-linear settings. In this talk we will introduce a theoretical framework for such models and then present recent results\, known as `Bernstein-von Mises theorems’\, that show that the posterior measures are approximated in function space by the Gaussian laws of solutions to certain SPDEs that involve the inverse Fisher information of the underlying statistical model. \n  \n  \nOrganizers: \nAnna KORBA (CREST)\, Vincent DIVOL (CREST)\, Jaouad MOURTADA (CREST) \n  \n  \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/richard-nickl-university-of-cambridge-statistical-inference-for-infinite-dimensional-dynamical-systems/
CATEGORIES:Seminars,Statistics
ATTACH;FMTTYPE=:
END:VEVENT
END:VCALENDAR