BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:A Bayesian approach to parameter identification in Turing systems 
 - Campillo-Funollet\, E (University of Sussex)
DTSTART:20151126T110000Z
DTEND:20151126T123000Z
UID:TALK62697@talks.cam.ac.uk
CONTACT:42080
DESCRIPTION:We present a methodology to identify parameters in Turing syst
 ems from noisy data. The Bayesian framework provides a rigorous interpreta
 tion of the prior knowledge and the noise\, resulting in an approximation 
 of the full probability distribution for the parameters\, given the data. 
 Although the numerical approximation of the full probability distribution\
 nis computationally expensive\, parallelised algorithms produce good appro
 ximations\nin a few hours. With the probability distribution at hand\, it 
 is straightforward to compute credible regions for the parameters. The met
 hodology is applied to a well-known Turing system: the Schnakenberg system
 .\n
LOCATION:Seminar Room 2\, Newton Institute Gatehouse
END:VEVENT
END:VCALENDAR