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Found 5 papers, 2 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.
Riemannian optimization for non-centered mixture of scaled Gaussian distributions
The proposed Riemannian gradient descent algorithm is leveraged to solve this second minimization problem.
Robust Geometric Metric Learning
This paper proposes new algorithms for the metric learning problem.
Riemannian geometry for Compound Gaussian distributions: application to recursive change detection
A new Riemannian geometry for the Compound Gaussian distribution is proposed.
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