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
	
		
		
			
		
					| Dates: | 22 September 2015 | 
							| Times: | 16:00 - 17:30 | 
	| What is it: | Seminar | 
	| Organiser: | School of Social Sciences | 
	
	
			
	| Who is it for: | University staff, Current University students, General public | 
		
				
				
			
			
			
	| Speaker: | Prof. Murray Aitkin | 
			
			
			
	   
	   
	    
	   
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	                	This talk discusses the assessment of the number of classes in a social network, through the latent class model extension of the exponential random graph model. The assessment uses a new Bayesian method of model comparisons, based on the posterior distribution of the deviance for each of the competing models.
The approach is applied to a well-known social network in which the number of classes is known a priori, and to the Noordin Top terrorist network, analysed at length in the book by Everton (2012).
The performance of the model comparison method is illustrated with simulations from  single population models and normal mixture models.
This work is joint with Duy Vu and Brian Francis.
References
Aitkin, M. (2010) Statistical Inference: an Integrated Bayesian/Likelihood Approach. CRC Press.
Aitkin, M., Vu, D. and Francis, B. (2014) Statistical modelling of the group structure of social networks. Social Networks 38, 74-87.
Aitkin, M., Vu, D. and Francis, B. (2015)  A new Bayesian approach for determining the number of components in a finite mixture. Metron (to appear).
DOI :10.1007/s40300-015-0068-1
Aitkin, M., Vu, D. and Francis, B. (2015) Statistical modelling of a terrorist network. Submitted.
Everton, S.F. (2012) Disrupting Dark Networks. Cambridge University Press.
No registration needed. All Welcome. Tea & coffee provided from 3.45
	 
	
		
		
		
	
	
		Speaker
			
				Prof. Murray Aitkin
				
Role: Professorial Fellow 
				Organisation: School of Mathematics and Statistics, University of Melbourne
				
				
			 
	 
	
	
		
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