Efficiently Learning Ising Models on Arbitrary Graphs
We consider the problem of reconstructing the graph underlying an Ising model from i.i.d. samples. Over the last fifteen years this problem has been of significant interest in the statistics, machine learning, and statistical physics communities, and much of the effort has been directed towards find...
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| Format: | Article |
| Language: | en_US |
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Association for Computing Machinery (ACM)
2017
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| Online Access: | http://hdl.handle.net/1721.1/110787 https://orcid.org/0000-0003-1303-582X |