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UID:20251120T205755EST-8217OVijmU@132.216.98.100
DTSTAMP:20251121T015755Z
DESCRIPTION:Title: Statistical Inference for partially observed branching p
 rocesses\, with application to hematopoietic lineage tracking\n\n \n\n\n	Ab
 stract:\n\n\nThe likelihood function is central to many statistical proced
 ures\, but poses challenges in classical and modern data settings. Motivat
 ed by cell lineage tracking experiments to study hematopoiesis (the proces
 s of blood cell production)\, we present recent methodology enabling likel
 ihood-based inference for partially observed data arising from continuous-
 time branching processes. These computational advances allow principled pr
 ocedures such as maximum likelihood estimation\, posterior inference\, and
  expectation-maximization (EM) algorithms in previously intractable data s
 ettings. We then discuss limitations and alternatives when data are very l
 arge or generated from a hidden process\, and potential ways forward using
  ideas from sparse optimization.\n\n\n	Speaker\n\n\nJason Xu is an Assistan
 t Professor of Statistical Science at Duke University. Prior to joining th
 e department\, he was supported by the NSF Mathematical Sciences Postdocto
 ral Research Fellowship at the University of California Los Angeles. He co
 mpleted his PhD in Statistics at the University of Washington advised by P
 rof. Vladimir Minin.\n
DTSTART:20190301T203000Z
DTEND:20190301T213000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Jason Xu (Duke University)
URL:/mathstat/channels/event/jason-xu-duke-university-
 294991
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