BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Linear Dynamical Systems Models of Adult Lifespan Data - Will Penn
 y\, University of East Anglia
DTSTART:20190909T113000Z
DTEND:20190909T123000Z
UID:TALK128428@talks.cam.ac.uk
CONTACT:Johan Carlin
DESCRIPTION:The Linear Dynamical Systems (LDS) model can be viewed as a ty
 pe of dynamic factor analysis in which the factors are fixed but the facto
 r loadings evolve according to a dynamical system. Maximum Likelihood mode
 l parameters can be estimated using Expectation-Maximisation (EM) or gradi
 ent-based methods\, and a Variational Bayes (VB ) approach has been develo
 ped that allows for inference over parameters and model dimension (e.g. ho
 w many factors). In this talk I'll described the application of VB-LDS to 
 cognitive\, white and gray matter data from Cam-CAN following the example 
 of de Mooij et al\, JoN\, 2018. I'll describe how we're updating the EM/VB
 -LDS algorithms to accommodate data sets with observations that are comple
 tely or partially missing or which have multiple observations at the same 
 time point (age).  I'll then propose that this methodology be used to inte
 grate cognitive neuroscience data across multiple databases and discuss wi
 th you the challenges involved.
LOCATION:Lecture Theatre\, MRC Cognition and Brain Sciences Unit\, 15 Chau
 cer Road\, Cambridge
END:VEVENT
END:VCALENDAR