Learning Mixed Graphical Models

2012/05/22 by Jason D. Lee, Trevor Hastie, Lee, Jason D. +1 · 2 citations
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Mathematics #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Medical Image Segmentation Techniques #Optimization and Control (math.OC)

paper · pdf · doi:10.48550/arxiv.1205.5012

openalex publication_date 2012/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and discrete variables that is amenable to structure learning. In previous work, authors have considered structure learning of Gaussian graphical models and structure learning of discrete models. Our approach is a natural generalization of these two lines of work to the mixed case. The penalization scheme involves a novel symmetric use of the group-lasso norm and follows naturally from a particular parametrization of the model.

Citations

• Learning mixed graphical models from data with p larger than n

Cited by

• CALM: A Causal Analysis Language Model for Tabular Data in Complex Systems with Local Scores, Conditional Independence Tests, and Relation Attributes
• Scalable Causal Discovery from Recursive Nonlinear Data via Truncated Basis Function Scores and Tests

Related

• Selective Inference and Learning Mixed Graphical Models
• Learning mixed graphical models from data with p larger than n
• Learning Pairwise Graphical Models with Nonlinear Sufficient Statistics
• Structure Learning in Graphical Modeling
• On Learning Discrete Graphical Models Using Greedy Methods