Applied Maths Seminar: Fajardo-Fontiveros -- Transitions in Bayesian model selection problems: link prediction on complex networks and symbolic regression

Dear all, 

Our upcoming AM seminar is held Wed 24 May at 1pm in F11 Chemistry Lecture Theatre 4.
Our speaker is Oscar Fajardo-Fontiveros (USYD).  

Talk details follow below: 

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Title: Transitions in Bayesian model selection problems: link prediction on complex
networks and symbolic regression 

Abstract: In this talk, I want to show you the effects of the prior and the likelihood
in Bayesian inference problems applied to model selection problems and how it can help
us understand some aspects of the results that we get.  To do that, first I am going to
show you the importance of both terms in the Bayesian inference process by showing you
how the balance in the likelihood and the prior are relevant in the models that we
choose.  After that I am going to show you a couple of applications of this framework: a
link prediction problem in complex networks and a symbolic regression problem.  In the
link prediction, I show how nodes metadata (extra information such as the age,
ethnicity, gender...)  produce a crossover of the types of models that we get with the
observed links, affecting accuracy.  In symbolic regression, I show that the noise of
the data and its size, produce a transition of the learnability of the ground true model
that generated our dataset.
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An ongoing list of AM seminars is posted here:
https://www.maths.usyd.edu.au/u/SemConf/Applied.html 

See you there, Ian