---
title: "Non-Parametric Mutual Information Estimation with Vine Copulas"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.07693v1"
permalink: "https://feed.astralor.com/items/itm_fa38c4264c274310858a82f9"
published: "2026-11-05T09:18:29.000Z"
score: 54
channel: "tech"
tags: ["Non-Parametric Mutual Information","Estimation","Vine Copulas","VINE","D-vine","Approximating"]
---

# Non-Parametric Mutual Information Estimation with Vine Copulas

## At a glance

We introduce VINE: a non-parametric mutual information (MI) estimator based on simplified D-vine copulas. Approximating the joint density with a simplified D-vine allows the density ratio to simplify to a product of pai…

## Key points

- We introduce VINE: a non-parametric mutual information (MI) estimator based on simplified D-vine copulas.
- Approximating the joint density with a simplified D-vine allows the density ratio to simplify to a product of pair copulas, which we can estimate non-parametrically.
- This structural simplification yields VINE, a flexible and computationally efficient MI estimator.

## Full text

We introduce VINE: a non-parametric mutual information (MI) estimator based on simplified D-vine copulas. Approximating the joint density with a simplified D-vine allows the density ratio to simplify to a product of pair copulas, which we can estimate non-parametrically. This structural simplification yields VINE, a flexible and computationally efficient MI estimator. We compare VINE against SotA neural MI estimators on a range of datasets, both under settings where the simplified D-vine assumption holds and where it is violated, demonstrating competitive accuracy at a lower computational cost for low- to medium-dimensional datasets.

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