Graphical Models for Discrete and Continuous Data

2016/09/18 by Rui Zhuang, Noah Simon, Zhuang, Rui +3
Computer Science · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Neural Networks and Applications #Other Statistics (stat.OT) #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1609.05551

openalex publication_date 2016/09/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We introduce a general framework for undirected graphical models. It generalizes Gaussian graphical models to a wide range of continuous, discrete, and combinations of different types of data. The models in the framework, called exponential trace models, are amenable to estimation based on maximum likelihood. We introduce a sampling-based approximation algorithm for computing the maximum likelihood estimator, and we apply this pipeline to learn simultaneous neural activities from spike data.

Citations

• Local and Global Inference for High Dimensional Nonparanormal Graphical Models

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