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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Rothschild Lecture: Mathematics for data-driven mo
deling - The science of crystal balls - Yannis Kev
rekidis (Princeton University)
DTSTART;TZID=Europe/London:20160621T160000
DTEND;TZID=Europe/London:20160621T170000
UID:TALK66535AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/66535
DESCRIPTION:In mathematical modeling one typically progresses
from observations of the world (and some serious t
hinking!) to equations for a model\, and then to t
he analysis of the model to make predictions. Good
\;mathematical models give good predictions (
and inaccurate ones do not) > - but the computati
onal tools for analyzing them are the same: algori
thms that are typically based on closed form equat
ions. While the skeleton of the process remains th
e same\, today we witness the \;development of
mathematical techniques that operate directly on
observations -data-\, and "circumvent" the serious
thinking that goes into selecting variables and p
arameters and writing equations. The process then
may appear to the user a little like making predic
tions by \; "looking into a crystal ball". Yet
the "serious thinking" is still there and uses th
e same -and some new- mathematics: it goes into&nb
sp\;building algorithms that "jump directly" from
data to the analysis of the model (which is never
available in closed form) so as to make prediction
s. I will present a couple of efforts that illustr
ate this new path from data to predictions. It rea
lly is the same old path\, but it is \;travell
ed by new means.
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:INI IT
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