Polyhedral Approach to Statistical Learning Graphical Models

DOI10.1142/9789814383462_0020zbMath1341.62190OpenAlexW2312714401MaRDI QIDQ2812506

David C. Haws, Milan Studený, Silvia Lindner, Raymond Hemmecke

Publication date: 16 June 2016

Published in: Harmony of Gröbner Bases and the Modern Industrial Society - The Second CREST-CSBM International Conference (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/2513f447f8dcf2b69afbd4883579aa4c421b0df3

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15) Applications of graph theory (05C90) Special polytopes (linear programming, centrally symmetric, etc.) (52B12) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (4)

Characteristic imsets for learning Bayesian network structure ⋮ Generalized Permutohedra from Probabilistic Graphical Models ⋮ Learning Bayesian network structure: towards the essential graph by integer linear programming tools ⋮ Polyhedral approaches to learning Bayesian networks

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