Constructing Composite Likelihoods in General Random FieldsDownload PDF

28 Mar 2024 (modified: 16 Apr 2013)ICML 2013 Inferning submissionReaders: Everyone
Decision: conferenceOral
Abstract: We propose a simple estimator based on composite likelihoods for parameter learning in random field models. The estimator can be applied to all discrete graphical models such as Markov random fields and conditional random fields, including ones with higher-order energies. It is computationally efficient because it requires only inference over tree-structured subgraphs of the original graph, and it is consistent, that is, it asymptotically gives the optimal parameter estimate in the model class. We verify these conceptual advantages in synthetic experiments and demonstrate the difficulties encountered by popular alternative estimation approaches.
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