Graphical Models for Discrete and Continuous Data

Rui Zhuang; Noah Simon; Johannes Lederer

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arXiv · 1609.05551

Graphical Models for Discrete and Continuous Data

Rui Zhuang·Noah Simon·Johannes Lederer

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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.

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Rui Zhuang, Noah Simon, Johannes Lederer. 2016-09-18. Graphical Models for Discrete and Continuous Data. https://arxiv.org/abs/1609.05551

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