Sloth in Austin
I'll be speaking at UT-Austin next week, through the kindness of the division of statistics and scientific computation:
- "When Can We Learn Network Models from Samples?"
- Abstract: Statistical models of network structure are models for the entire network, but the data are typically just a sampled sub-network. Parameters for the whole network, which are what we care about, are estimated by fitting the model on the sub-network. This assumes that the model is "consistent under sampling" (forms a projective family). For the widely-used exponential random graph models (ERGMs), this trivial-looking condition is violated by many popular and scientifically appealing models; satisfying it drastically limits ERGMs' expressive power. These results are special cases of more general ones about exponential families of dependent variables, which we also prove. As a consolation prize, we offer easily checked conditions for the consistency of maximum likelihood estimation in ERGMs, and discuss some possible constructive responses.
- Time and place: 2--3 pm on Wednesday, 11 January 2012, in Hogg Building (WCH), room 1.108
This will of course be based on my paper with
Alessandro, but since I understand some non-statisticians may sneak in,
I'll try to be more comprehensible and less technical.
Since this will be my first time in Austin (indeed my first time in Texas),
and I have (for a wonder) absolutely no obligations on the 12th, suggestions on
what I should see or do would be appreciated.
Posted at January 06, 2012 14:15 | permanent link