Copula Density Neural Estimation

Fuente: arXiv
Salvato in:
Dettagli Bibliografici
Autori principali: Letizia, Nunzio A., Novello, Nicola, Tonello, Andrea M.
Natura: Preprint
Pubblicazione: 2022
Soggetti:
Accesso online:
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
_version_ 1866916830348574720
author Letizia, Nunzio A.
Novello, Nicola
Tonello, Andrea M.
author_facet Letizia, Nunzio A.
Novello, Nicola
Tonello, Andrea M.
contents Probability density estimation from observed data constitutes a central task in statistics. In this brief, we focus on the problem of estimating the copula density associated to any observed data, as it fully describes the dependence between random variables. We separate univariate marginal distributions from the joint dependence structure in the data, the copula itself, and we model the latter with a neural network-based method referred to as copula density neural estimation (CODINE). Results show that the novel learning approach is capable of modeling complex distributions and can be applied for mutual information estimation and data generation.
format Preprint
id arxiv_https___arxiv_org_abs_2211_15353
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Copula Density Neural Estimation
Letizia, Nunzio A.
Novello, Nicola
Tonello, Andrea M.
Machine Learning
Signal Processing
Probability density estimation from observed data constitutes a central task in statistics. In this brief, we focus on the problem of estimating the copula density associated to any observed data, as it fully describes the dependence between random variables. We separate univariate marginal distributions from the joint dependence structure in the data, the copula itself, and we model the latter with a neural network-based method referred to as copula density neural estimation (CODINE). Results show that the novel learning approach is capable of modeling complex distributions and can be applied for mutual information estimation and data generation.
title Copula Density Neural Estimation
topic Machine Learning
Signal Processing
url https://arxiv.org/abs/2211.15353