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pro vyhledávání: '"Sagar, Ksheera"'
Sparse structure learning in high-dimensional Gaussian graphical models is an important problem in multivariate statistical signal processing; since the sparsity pattern naturally encodes the conditional independence relationship among variables. How
Externí odkaz:
http://arxiv.org/abs/2303.06914
Bayesian Covariate-Dependent Quantile Directed Acyclic Graphical Models for Individualized Inference
We propose an approach termed ``qDAGx'' for Bayesian covariate-dependent quantile directed acyclic graphs (DAGs) where these DAGs are individualized, in the sense that they depend on individual-specific covariates. The individualized DAG structure of
Externí odkaz:
http://arxiv.org/abs/2210.08096
Autor:
Sagar, Ksheera, Bhadra, Anindya
The horseshoe prior, defined as a half Cauchy scale mixture of normal, provides a state of the art approach to Bayesian sparse signal recovery. We provide a new representation of the horseshoe density as a scale mixture of the Laplace density, explic
Externí odkaz:
http://arxiv.org/abs/2209.04510
Marginal likelihood, also known as model evidence, is a fundamental quantity in Bayesian statistics. It is used for model selection using Bayes factors or for empirical Bayes tuning of prior hyper-parameters. Yet, the calculation of evidence has rema
Externí odkaz:
http://arxiv.org/abs/2205.01016
Precision matrix estimation in a multivariate Gaussian model is fundamental to network estimation. Although there exist both Bayesian and frequentist approaches to this, it is difficult to obtain good Bayesian and frequentist properties under the sam
Externí odkaz:
http://arxiv.org/abs/2104.10750
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Akademický článek
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Marginal likelihood, also known as model evidence, is a fundamental quantity in Bayesian statistics. It is used for model selection using Bayes factors or for empirical Bayes tuning of prior hyper-parameters. Yet, the calculation of evidence has rema
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d4ad0e68b0734a5b348995eb5cafb4aa