BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//wp-events-plugin.com//7.2.3.1//EN
BEGIN:VEVENT
UID:628@lincs.fr
DTSTART;TZID=Europe/Paris:20210428T110000
DTEND;TZID=Europe/Paris:20210428T120000
DTSTAMP:20210429T120250Z
URL:https://www.lincs.fr/events/exponential-families/
SUMMARY:Exponential families
DESCRIPTION:Exponential families are parametric sets of probability
 distributions that arise in many applications. These include well-known
 univariate distributions (such as the binomial\, Poisson\, geometric\,
 exponential\, and normal distributions)\, but also multi-variate
 distributions like probabilistic graphical models and stationary
 distributions of several queueing models. In this presentation\, we will
 first recall the definition of exponential families and motivate their
 study. In a second time\, we will present a generic method for
 approximating the normalization constant of these distributions\, as the
 exact calculation of this constant is practically infeasible in high
 dimension.\n\nReferences:\n\n 	M. J. Wainwright and M. I. Jordan. Graphical
 Models\, Exponential Families\, and Variational Inference. Foundations and
 Trends® in Machine Learning 1.1 (2008)\,
 https://people.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf.
 Chapters 1\, 2\, and 3 and Appendix A.\n 	S. Boyd and L. Vandenberghe.
 Convex Optimization. Cambridge University Press (2004)\,
 https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf. Section 3.3.\n
 	Wikipedia pages Exponential family\, Maximum-entropy probability
 distribution\, Principle of maximum entropy\, Lagrange multiplier\, Convex
 conjugate.\n\n\nSlides
CATEGORIES:Network Theory,Working Group,Youtube
END:VEVENT
BEGIN:VTIMEZONE
TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
BEGIN:DAYLIGHT
DTSTART:20210328T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
END:DAYLIGHT
END:VTIMEZONE
END:VCALENDAR