# Learning Clique Forests

@article{Massara2019LearningCF, title={Learning Clique Forests}, author={Guido Previde Massara and Tomaso Aste}, journal={ArXiv}, year={2019}, volume={abs/1905.02266} }

We propose a topological learning algorithm for the estimation of the conditional dependency structure of large sets of random variables from sparse and noisy data. The algorithm, named Maximally Filtered Clique Forest (MFCF), produces a clique forest and an associated Markov Random Field (MRF) by generalising Prim's minimum spanning tree algorithm. To the best of our knowledge, the MFCF presents three elements of novelty with respect to existing structure learning approaches. The first is the…

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