Proof of a Conjecture of Drton, Sturmfels and Sullivant on the maximum
 likelihood degree of the Gaussian graphical model of a cycle

Q: What type of study is this?

This is a Quantitative Study study.

Rodica Dinu; Martin Vodička

doi:10.48550/arxiv.2503.02704

March 4, 2025Open Access

Proof of a Conjecture of Drton, Sturmfels and Sullivant on the maximum likelihood degree of the Gaussian graphical model of a cycle

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Key Points

The maximum likelihood degree of the Gaussian graphical model for cycles was precisely calculated, affirming the conjecture made by Drton, Sturmfels, and Sullivant.
The findings reveal that the previously speculated maximum likelihood degree holds true for Gaussian graphical models involving cycles.
This study involved a mathematical proof that supports theoretical advancements in understanding graphical models.
The results may further develop the statistical properties within the realm of Gaussian graphical models and their applications.

Implication

This analysis computes the maximum likelihood degree in Gaussian graphical models of cycles, confirming previous conjectures implying advancements in theoretical statistics.

Abstract

In this article, we compute the precise value of the maximum likelihood degree of the Gaussian graphical model of a cycle, confirming a conjecture due to Drton, Sturmfels and Sullivant.

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Cite This Study

Dinu et al. (2025) studied this question.

synapsesocial.com/papers/68f5fcce8d54a28a75cf1ae9 https://doi.org/10.48550/arxiv.2503.02704

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Proof of a Conjecture of Drton, Sturmfels and Sullivant on the maximum likelihood degree of the Gaussian graphical model of a cycle

Key Points

Implication

Abstract

Discussion

Cite This Study

Proof of a Conjecture of Drton, Sturmfels and Sullivant on the maximum likelihood degree of the Gaussian graphical model of a cycle

Key Points

Implication

Abstract

Discussion

Cite This Study

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