A Localization Approach to Improve Iterative Proportional Scaling in Gaussian Graphical Models

DOI10.1080/03610920802238662zbMath1318.62173arXiv0802.2581OpenAlexW2077349636MaRDI QIDQ3585266

Hisayuki Hara, Akimichi Takemura

Publication date: 19 August 2010

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0802.2581

zbMATH Keywords

maximum likelihoodchordal graphcovariance selectionchordal extension

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Measures of association (correlation, canonical correlation, etc.) (62H20) Graphical methods in statistics (62A09)

Related Items (4)

Local computations of the iterative proportional scaling procedure for hierarchical models ⋮ An improved Hara-Takamura procedure by sharing computations on junction tree in Gaussian graphical models ⋮ Standard imsets for undirected and chain graphical models ⋮ Multiplicity adjustment for temporal and spatial scan statistics using Markov property

Uses Software

• MIM

Cites Work

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