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[[en:seminaires:SeminaireDuLPSM:index|LPSM Seminar]]\\
Thursday March 19, 2026, 9:30AM, A préciser\\
**Richard Nickl** (University of Cambridge) //Statistical inference for infinite-dimensional dynamical systems//
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A common task in `data assimilation’ is 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 many important 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, almost nothing is known about the statistical behaviour of these posterior measures in non-linear setting. In this talk we will introduce the framework 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.
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[[en:seminaires:SeminaireDuLPSM:index|LPSM Seminar]]\\
Monday May 4, 2026, 9:30AM, A préciser\\
**Tom Hutchcroft**  //A préciser//
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