Parameterising Gaussian Graphical Models

2026-08-31

AI Summary

本文通过将精度矩阵分解为边际方差和偏相关性等尺度不变参数，解决了高斯图形模型中因变量尺度不同导致的模型选择问题。

Abstract

Gaussian graphical models (GGMs) describe the dependence structure among jointly Gaussian random variables. However, the most common parameterisation of GGMs, the precision matrix, describes both the dependence and scale of the variables. This has been shown to lead to model selection methods that depend on the scale of the variables, despite graphical models being scale invariant. Even after standardising data to have unit sample variances, entries of the precision matrix can be on different scales leading to poor model selection. This paper decomposes the precision matrix into marginal variances and interpretable scale-invariant parameters - the partial correlations and variance inflation factors. This decomposition gives new insights into the precision matrix entries and the dynamics of model selection methods such as penalised likelihoods. In particular, it explains the observed phenomenon that penalties on the precision matrix perform poorly at selecting hub variables and motivates the necessity of data standardisation. It also shows why methods based on partial correlations have better hub detection properties. The effect of penalisation of different quantities on the estimation of marginal variances is then investigated and an interesting simplification of the log-likelihood is shown when using maximum likelihood estimation of the marginal variances.

Problem

• Gaussian Graphical Models
• precision matrix
• scale invariance
• model selection

Innovation

• Gaussian Graphical Models
• Precision Matrix Decomposition
• Partial Correlations
• Variance Inflation Factors
• Model Selection