A loss‐based prior for Gaussian graphical models

Author:

Hinoveanu Laurenţiu Cătălin1,Leisen Fabrizio2,Villa Cristiano3

Affiliation:

1. School of Mathematics, Statistics and Actuarial Science University of Kent Sibson Building Canterbury CT2 7FSUK

2. School of Mathematical Sciences University of Nottingham University Park Nottingham NG7 2RDUK

3. School of Mathematics, Statistics and Physics Newcastle University Herschel Building Newcastle NE1 7RUUK

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference51 articles.

1. Inferring sparse Gaussian graphical models with latent structure

2. Bayesian covariance matrix estimation using a mixture of decomposable graphical models

3. A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models

4. Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data;Banerjee O.;The Journal of Machine Learning Research,2008

5. Bell P.&King S.(2007).Sparse Gaussian graphical models for speech recognition. InINTERSPEECH 2007 8th Annual Conference of the International Speech Communication Association Antwerp Belgium August 27–31 2007 pp. 2113–2116.

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1. Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models;Journal of the American Statistical Association;2021-12-21

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