BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Statistical analysis and optimality of biological systems - Prof G
 asper Tkacik
DTSTART:20210422T140000Z
DTEND:20210422T150000Z
UID:TALK158668@talks.cam.ac.uk
CONTACT:Sarah Morgan
DESCRIPTION:Normative theories and statistical inference provide complemen
 tary approaches for the study of biological systems. A normative theory po
 stulates that organisms have adapted to efficiently solve essential tasks 
 and proceeds to mathematically work out testable consequences of such opti
 mality\; parameters that maximize the hypothesized organismal function can
  be derived ab initio\, without reference to experimental data. In contras
 t\, statistical inference focuses on the efficient utilization of data to 
 learn model parameters\, without reference to any a priori notion of biolo
 gical function. Traditionally\, these two approaches were developed indepe
 ndently and applied separately. Here\, we unify them in a coherent Bayesia
 n framework that embeds a normative theory into a family of maximum-entrop
 y “optimization priors.” This family defines a smooth interpolation be
 tween a data-rich inference regime and a data-limited prediction regime. I
  will illustrate how this framework can productively guide our thinking on
  several neuroscience and non-neuroscience examples.
LOCATION:Online
END:VEVENT
END:VCALENDAR