Work

Bayesian estimation of the Kullback-Leibler divergence for categorical systems using mixtures of Dirichlet priors

Year: 2024

Type: article

Source: Physical review. E

DOI: 10.1103/physreve.109.024305

Authors: Francesco Camaglia, Ilya Nemenman, Thierry Mora, Aleksandra M. Walczak

Institutions: Centre National de la Recherche Scientifique, Université Paris Cité, Université Paris Sciences et Lettres, Sorbonne Université, Laboratoire de Physique de l'Ecole Normale Supérieure, Emory University

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

Funders: National Science Foundation, Simons Foundation, Agence Nationale de la Recherche, European Research Council

Awards: G2636601749, G5179006044, NSF-ANR: Precision of collective, distributed, dynamic gene regulation, Computational analysis of the response of immune repertoires to viruses

OpenAlex ID: W4391776255

FWCI: 0.889

Cites: 28

Cited by: 6

Related to: 10