Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
zbMath1222.68229OpenAlexw2155573334MaRDI QIDQ6482960
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Publication date: March 2007
Published in: Journal of Machine Learning Research (Search for Journal in Brave)
Full work available at URL: https://jmlr.org/papers/volume8/kalisch07a/kalisch07a.pdf
Description: We consider the PC-algorithm for estimating the skeleton of a very high-dimensional acyclic directed graph (DAG) with corresponding Gaussian distribution. The PC-algorithm is computationally feasible for sparse problems with many nodes, i.e. variables, and it has the attractive property to automatically achieve high computational efficiency as a function of sparseness of the true underlying DAG. We prove consistency of the algorithm for very high-dimensional, sparse DAGs where the number of nodes is allowed to quickly grow with sample size n, as fast as O(n^a) for any 0.
Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10)
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