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SUMMARY:A nonparametric problem for stochastic PDEs - Randolf Altmeyer
DTSTART:20200617T130000Z
DTEND:20200617T140000Z
UID:TALK142744@talks.cam.ac.uk
CONTACT:J.W.Stevens
DESCRIPTION:Stochastic partial differential equations (SPDEs) are models f
 or random processes evolving in time and space. They can be used to descri
 be various phenomena\, for example in neuroscience\, oceanography\, biophy
 sics and finance. The practical implementation of SPDE models\, however\, 
 hinges on a good understanding of the underlying information structure (in
  time and space) and on the development of robust statistical methods. In 
 this talk\, I will discuss the basic ideas for this analysis and explain w
 hat difficulties arise from the spatial structure. Based on recent results
  in (1)\, the focus will be on nonparametric inference for spatially local
 ized measurements and the challenging journey to a suitable Bernstein-von-
 Mises theorem in the context of Bayesian statistics.\n\n(1) R. Altmeyer\, 
 M. Reiss (2020). Nonparametric estimation for linear SPDEs from local meas
 urements. Annals of\nApplied Probability\, to appear. arXiv preprint arXiv
 :1903.06984.\n\n\n"Join Zoom Meeting":https://us02web.zoom.us/j/8934800701
 5?pwd=UnlhN0dLQ1BHQlJLZUlhWUlkQkhCUT09\n\nMeeting ID: 893 4800 7015\nPassw
 ord: 46Y235CF
LOCATION:Virtual Zoom meeting
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