Afficher la couverture 'Beetroots (BayEsian infErence with spaTial Regularization of nOisy multi-line ObservaTion mapS) | Palud, Pierre' en grand format
/5

0 avis

Beetroots (BayEsian infErence with spaTial Regularization of nOisy multi-line ObservaTion mapS)

Archive ouverte

Palud, Pierre | Thouvenin, Pierre-Antoine | Chainais, Pierre | Bron, Emeric, E. | Le Petit, Franck

Edité par CCSD -

Beetroots (BayEsian infErence with spaTial Regularization of nOisy multi-line ObservaTion mapS) is a Python package that performs Bayesian inference of physical parameters from multispectral-structured cubes with a dedicated sampling algorithm. Thanks to this sampling algorithm, beetroots provides maps of credibility intervals along with estimated maps.

Suggestions

Du même auteur

Problèmes inverses et test bayésien d'adéquation du modèle

Archive ouverte | Palud, Pierre | CCSD

National audience. La résolution d'un problème inverse est basée sur le choix d'un modèle, dont la qualité affecte fortement la pertinence des estimateurs. Ce travail présente une méthode d'identification des cas où...

Efficient Sampling of Non Log-Concave Posterior Distributions With Mixture of Noises

Archive ouverte | Palud, Pierre | CCSD

International audience. This article focuses on a challenging class of inverse problems that is often encountered in applications. The forward model is a complex non-linear black-box, potentially non-injective, whos...

Neural network-based emulation of interstellar medium models

Archive ouverte | Palud, Pierre | CCSD

International audience. Context. The interpretation of observations of atomic and molecular tracers in the galactic and extragalactic interstellar medium (ISM) requires comparisons with state-of-the-art astrophysica...

Chargement des enrichissements...