# Log-concave density estimation in undirected graphical models

arXiv: https://arxiv.org/abs/2206.05227 · HTML: https://mlsanity.com/paper/2206.05227/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 42 references that are in the map.
- First posted on arXiv 10 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Recent progress in log-concave density estimation](https://mlsanity.com/paper/1709.03154/) (2017)
- [A new computational framework for log-concave density estimation](https://mlsanity.com/paper/2105.11387/) (2021)
- [Selection and Estimation for Mixed Graphical Models](https://mlsanity.com/paper/1311.0085/) (2013)
- [Geometry of Log-Concave Density Estimation](https://mlsanity.com/paper/1704.01910/) (2017)
- [Exact Solutions in Log-Concave Maximum Likelihood Estimation](https://mlsanity.com/paper/2003.04840/) (2020)

## Abstract (arXiv)

We study the problem of maximum likelihood estimation of densities that are log-concave and lie in the graphical model corresponding to a given undirected graph G. We show that the maximum likelihood estimate (MLE) is the product of the exponentials of several tent functions, one for each maximal clique of G. While the set of log-concave densities in a graphical model is infinite-dimensional, our results imply that the MLE can be found by solving a finite-dimensional convex optimization problem. We provide an implementation and a few examples. Furthermore, we show that the MLE exists and is unique with probability 1 as long as the number of sample points is larger than the size of the largest clique of G when G is chordal. We show that the MLE is consistent when the graph G is a disjoint union of cliques. Finally, we discuss the conditions under which a log-concave density in the graphical model of G has a log-concave factorization according to G.