Global multivariate model learning from hierarchically correlated data

DOI10.1088/1742-5468/ac06c2OpenAlexW3185769150MaRDI QIDQ3383366

Pierre Barrat-Charlaix, E. Rodriguez-Horta, Alejandro Lage-Castellanos, Martin Weigt

Publication date: 23 September 2021

Published in: Journal of Statistical Mechanics: Theory and Experiment (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2102.06036

zbMATH Keywords

phylogenyco-evolutioninference of graphical modelsstatistical inference in biological systems

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Related Items (1)

Ancestral sequence reconstruction for co-evolutionary models

Uses Software

• L-BFGS
• NLopt
• PSICOV
• UniProt

Cites Work

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• Fast likelihood calculation for multivariate Gaussian phylogenetic models with shifts
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• On the limited memory BFGS method for large scale optimization
• The physics of flocking: correlation as a compass from experiments to theory
• Information Theory and Statistical Mechanics
• Updating Quasi-Newton Matrices with Limited Storage
• Biological Sequence Analysis
• An evolution-based model for designing chorismate mutase enzymes

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