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DTSTAMP:20250805T192253Z
DESCRIPTION:Conditional density estimation through optimal transport.\n\nCo
 nditional probability estimation and simulation provides data-based answer
 s to all kinds of critical questions\, such as the response of specific pa
 tients to different medical treatments\, the effect of political measures 
 on the economy\, and weather and climate forecasts. In the complex systems
  behind these examples\, the outcome of a process depends on many and dive
 rse factors and is probabilistic in nature\, due in part to our ignorance 
 of other relevant factors and to the chaotic nature of the underlying dyna
 mics.\n\nThis talk will describe a general procedure for the estimation an
 d simulation of conditional probabilities based on two complementary ideas
 : the removal of the effect of covariates through a data-based\, generaliz
 ed optimal transport barycenter problem\, and the reduction of complexity 
 through a low-rank tensor factorization/separation of variables procedure 
 extended to variables of any type.\n
DTSTART:20171016T200000Z
DTEND:20171016T210000Z
LOCATION:Room 920\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue 
 Sherbrooke Ouest
SUMMARY:Esteban Tabak\, Courant Institute
URL:/mathstat/channels/event/esteban-tabak-courant-ins
 titute-276209
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