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Found 6 papers, 1 papers with code
Natural Bayesian Cramér-Rao Bound with an Application to Covariance Estimation
In this paper, we propose to develop a new Cram\'er-Rao Bound (CRB) when the parameter to estimate lies in a manifold and follows a prior distribution.
The Fisher-Rao geometry of CES distributions
When dealing with a parametric statistical model, a Riemannian manifold can naturally appear by endowing the parameter space with the Fisher information metric.
Learning Graphical Factor Models with Riemannian Optimization
Graphical models and factor analysis are well-established tools in multivariate statistics.
Riemannian classification of EEG signals with missing values
This paper proposes a strategy to handle missing data for the classification of electroencephalograms using covariance matrices.
Riemannian geometry for Compound Gaussian distributions: application to recursive change detection
A new Riemannian geometry for the Compound Gaussian distribution is proposed.
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
Relying on recent advances in statistical estimation of covariance distances based on random matrix theory, this article proposes an improved covariance and precision matrix estimation for a wide family of metrics.
