Abstract Details
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Activity Number:
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665
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Type:
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Invited
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Date/Time:
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Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Host Chapter-Montreal
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| Abstract - #307050 |
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Title:
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Asymptotic Inference for Gaussian Hidden Tree Models
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Author(s):
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Piotr Zwiernik*+
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Companies:
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UC Berkeley
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Keywords:
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singular BIC ;
tree models ;
Gaussian models ;
model geometry ;
model identifiability
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Abstract:
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Hidden tree models are Bayesian networks on rooted trees with all inner nodes representing data which are unobserved. Models of this type can be for example found in phylogenetics and they are complicated both from algebraic and statistical point of view. The likelihood function is multimodal, estimates of the parameters lie often on the boundary of the parameter space. The fact that the model is not identifiable rises many additional issues. In this talk we show how singular learning theory can be used to get a better insight into asymptotic analysis of these models in the case when studied system is multivariate Gaussian.
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Authors who are presenting talks have a * after their name.
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