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Titel:

Marginal likelihood and model selection for Gaussian latent tree and forest models

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Drton, Mathias; Lin, Shaowei; Weihs, Luca; Zwiernik, Piotr
Abstract:
Gaussian latent tree models, or more generally, Gaussian latent forest models have Fisher-information matrices that become singular along interesting submodels, namely, models that correspond to subforests. For these singularities, we compute the real log-canonical thresholds (also known as stochastic complexities or learning coefficients) that quantify the large-sample behavior of the marginal likelihood in Bayesian inference. This provides the information needed for a recently introduced gener...     »
Stichworte:
algebraic statistics, Gaussian graphical model, latent tree models, marginal likelihood, multivariate normal distribution, singular learning theory
Dewey Dezimalklassifikation:
510 Mathematik
Zeitschriftentitel:
Bernoulli
Jahr:
2017
Band / Volume:
23
Jahr / Monat:
2017-02
Quartal:
1. Quartal
Monat:
Feb
Heft / Issue:
2
Seitenangaben Beitrag:
1202-1232
Sprache:
en
Volltext / DOI:
doi:10.3150/15-bej775
WWW:
Project Euclid
Verlag / Institution:
Bernoulli Society for Mathematical Statistics and Probability
E-ISSN:
1350-7265
Publikationsdatum:
01.05.2017
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