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Rebecca Morrison

2 accepted papers

2025

Learning Local Neighborhoods of Non-Gaussian Graphical Models

AAAI 2025technical

Identifying the Markov properties or conditional independencies of a collection of random variables is a fundamental task in statistics for modeling and inference. Existing approaches often learn the structure of a probabilistic graph, which encodes these dependencies, by assuming that the variables…

2017

Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting

NeurIPS 2017poster

We present an algorithm to identify sparse dependence structure in continuous and non-Gaussian probability distributions, given a corresponding set of data. The conditional independence structure of an arbitrary distribution can be represented as an undirected graph (or Markov random field), but mos…

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