BEGIN:VCALENDAR
VERSION:2.0
PRODID:www.dresden-science-calendar.de
METHOD:PUBLISH
CALSCALE:GREGORIAN
X-MICROSOFT-CALSCALE:GREGORIAN
X-WR-TIMEZONE:Europe/Berlin
BEGIN:VTIMEZONE
TZID:Europe/Berlin
X-LIC-LOCATION:Europe/Berlin
BEGIN:DAYLIGHT
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
DTSTART:19810329T030000
RRULE:FREQ=YEARLY;INTERVAL=1;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
DTSTART:19961027T030000
RRULE:FREQ=YEARLY;INTERVAL=1;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:DSC-22642
DTSTART;TZID=Europe/Berlin:20260212T150000
SEQUENCE:1770878199
TRANSP:OPAQUE
DTEND;TZID=Europe/Berlin:20260212T160000
URL:https://dresden-science-calendar.org/calendar/de/detail/22642
LOCATION:MPI-CBG\, Pfotenhauerstraße 10801307 Dresden
SUMMARY:Semnani: Path-Dependent SDEs: Solutions and Parameter Estimation
CLASS:PUBLIC
DESCRIPTION:Speaker: Pardis Semnani\nInstitute of Speaker: University of Br
 itish Columbia\nTopics:\n\n Location:\n  Name: MPI-CBG (MPI-CBG CSBD SR To
 p Floor (VC))\n  Street: Pfotenhauerstraße 108\n  City: 01307 Dresden\n  
 Phone: +49 351 210-0\n  Fax: +49 351 210-2000\nDescription: In this talk\,
  we discuss how temporal causal structures can be modelled using a path-de
 pendent stochastic differential equation (SDE). We then consider a rich cl
 ass of path-dependent SDEs\, called signature SDEs\, which can model gener
 al path-dependent phenomena. We provide conditions that ensure the existen
 ce and uniqueness of solutions to a general signature SDE. Path signatures
  are iterated integrals of a given path with the property that any suffici
 ently nice function of the path can be approximated by a linear functional
  of its signatures. This is why we model the drift and diffusion of our si
 gnature SDE as linear functions of path signatures\, and then introduce th
 e Expected Signature Matching Method (ESMM) for linear signature SDEs\, wh
 ich enables inference of the signature-dependent drift and diffusion coeff
 icients from observed trajectories. Furthermore\, we show that the ESMM is
  consistent: given sufficiently many samples and Picard iterations used by
  the method\, the parameters estimated by the ESMM approach the true param
 eter with arbitrary precision. We discuss the asymptotic distribution of t
 he estimator obtained from the ESMM\, and finally\, demonstrate on a varie
 ty of empirical simulations that our ESMM accurately infers the drift and 
 diffusion parameters from observed trajectories. While parameter estimatio
 n is often restricted by the need for a suitable parametric model\, this s
 tudy makes progress toward a completely general framework for SDE paramete
 r estimation\, using signature terms to model arbitrary path-independent a
 nd path-dependent processes.This talk is based on joint work with Vincent 
 Guan\, Elina Robeva\, and Darrick Lee.
DTSTAMP:20260308T014512Z
CREATED:20260205T063608Z
LAST-MODIFIED:20260212T063639Z
END:VEVENT
END:VCALENDAR