BEGIN:VCALENDAR
PRODID:-//Columba Systems Ltd//NONSGML CPNG/SpringViewer/ICal Output/3.3-
M3//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20150916T121443Z
DTSTART:20150922T150000Z
DTEND:20150922T163000Z
SUMMARY:CMIST afternoon seminar: How many classes? Statistical modelling
of a social network and a terrorist network\, with a latent class model
and Bayesian model comparisons
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}i2e-ielh9vm
7-apvq68
DESCRIPTION:\n\nThis talk discusses the assessment of the number of class
es in a social network\, through the latent class model extension of the
exponential random graph model. The assessment uses a new Bayesian meth
od of model comparisons\, based on the posterior distribution of the dev
iance for each of the competing models.\n\nThe approach is applied to a
well-known social network in which the number of classes is known a prio
ri\, and to the Noordin Top terrorist network\, analysed at length in th
e book by Everton (2012).\n\nThe performance of the model comparison met
hod is illustrated with simulations from single population models and n
ormal mixture models.\n\nThis work is joint with Duy Vu and Brian Franci
s.\n\nReferences\nAitkin\, M. (2010) Statistical Inference: an Integrate
d Bayesian/Likelihood Approach. CRC Press.\nAitkin\, M.\, Vu\, D. and Fr
ancis\, B. (2014) Statistical modelling of the group structure of social
networks. Social Networks 38\, 74-87.\nAitkin\, M.\, Vu\, D. and Franci
s\, B. (2015) A new Bayesian approach for determining the number of com
ponents in a finite mixture. Metron (to appear).\nDOI :10.1007/s40300-01
5-0068-1\nAitkin\, M.\, Vu\, D. and Francis\, B. (2015) Statistical mode
lling of a terrorist network. Submitted.\nEverton\, S.F. (2012) Disrupti
ng Dark Networks. Cambridge University Press.\n\n\nNo registration neede
d. All Welcome. Tea & coffee provided from 3.45
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:2.07\, Humanities Bridgeford Street\, Manchester
END:VEVENT
END:VCALENDAR