BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Date iCal//NONSGML kigkonsult.se iCalcreator 2.20.2//
METHOD:PUBLISH
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:STANDARD
DTSTART:20181104T020000
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20190310T020000
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:calendar.23008.field_event_date_2.0@math.wustl.edu
CREATED:20181109T170222Z
DESCRIPTION:Abstract: Recent years have seen rapid growth in the volume of
observational and experimental data acquired from physical\, biological or
engineering systems. A fundamental question in several areas of science\,
engineering\, medicine\, and beyond is how to extract insight and knowled
ge from all of those available data. This process of learning from data is
at its core a mathematical inverse problem. That is\, given (possibly noi
sy) data and a (possibly uncertain) forward model describing the map from
parameters to data\, we seek to reconstruct or infer the parameters that c
haracterize the model. Inverse problems are often ill-posed\, i.e. their s
olution may not exist or may not be unique or may be unstable to perturbat
ion in the data. Simply put\, there may not be enough information in the d
ata to fully determine the model parameters. In these cases\, uncertainty
is a fundamental feature of the inverse problem. The goal then is to both
reconstruct the model parameters and quantify the uncertainty in such reco
nstruction. The ability to quantify these uncertainties is crucial to reli
ably predict the future behavior of the physical\, biological or engineeri
ng systems\, and to make informed decisions under uncertainty. This talk w
ill illustrate the mathematical concepts and computational tools necessary
for the solution of inverse problems in a deterministic and probabilistic
(Bayesian) framework. Examples of inverse problems arising in imaging\, g
eoscience\, material engineering\, and other fields of science will be pre
sented. \n\nHost: John McCarthy
DTSTART;TZID=America/Chicago:20190128T150000
DTEND;TZID=America/Chicago:20190128T160000
LAST-MODIFIED:20190115T222221Z
SUMMARY:Analysis Seminar: 'Learning from data through the lens of mathemati
cal models: a gentle introduction to Bayesian Inverse Problems'
URL;TYPE=URI:https://math.wustl.edu/events/analysis-seminar-learning-data-t
hrough-lens-mathematical-models-gentle-introduction-bayesian
END:VEVENT
END:VCALENDAR