Learning high-dimensional Markov forest distributions: Analysis of error rates
The problem of learning forest-structured discrete graphical models from i.i.d. samples is considered. An algorithm based on pruning of the Chow-Liu tree through adaptive thresholding is proposed. It is shown that this algorithm is both structurally consistent and risk consistent and the error proba...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
MIT Press
2011
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| Online Access: | http://hdl.handle.net/1721.1/66514 https://orcid.org/0000-0003-0149-5888 |