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SUMMARY:Approximating conditional distributions - Alessandra Cipriani (Bat
 h)
DTSTART:20171017T151500Z
DTEND:20171017T161500Z
UID:TALK82311@talks.cam.ac.uk
CONTACT:Perla Sousi
DESCRIPTION:There is a wealth of methods to bound the distance between pro
 bability laws. However\, when we want to compare two laws conditioned on t
 he outcome of some random experiment\, several difficulties arise. On the 
 one hand\, conditioning introduces strong dependence between the different
  components of a model\, and most of the methods work well in a regime of 
 weak dependence. However\, quite surprisingly\, there are also many situat
 ions when very different laws have similar\, or even identical conditional
  distributions. In this talk\, we discuss the basic ideas of a general pro
 cedure to adapt Stein's method to bound the distance between conditional d
 istributions and present two fundamental applications\, where conditional 
 laws arise naturally: the case of random walk bridges and the filtering eq
 uation. Joint work with A. Chiarini (ETH Zurich) and G. Conforti (École P
 olytechnique).​
LOCATION:MR12\, CMS\, Wilberforce Road\, Cambridge\, CB3 0WB
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