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- W1832558269 abstract "In recent years a number of methods have been developed for automatically learning the (sparse) connectivity structure of Markov Random Fields. These methods are mostly based on L1-regularized optimization which has a number of disadvantages such as the inability to assess model uncertainty and expensive cross-validation to find the optimal regularization parameter. Moreover, the model's predictive performance may degrade dramatically with a suboptimal value of the regularization parameter (which is sometimes desirable to induce sparseness). We propose a fully Bayesian approach based on a spike and slab prior (similar to L0 regularization) that does not suffer from these shortcomings. We develop an approximate MCMC method combining Langevin dynamics and reversible jump MCMC to conduct inference in this model. Experiments show that the proposed model learns a good combination of the structure and parameter values without the need for separate hyper-parameter tuning. Moreover, the model's predictive performance is much more robust than L1-based methods with hyper-parameter settings that induce highly sparse model structures." @default.
- W1832558269 created "2016-06-24" @default.
- W1832558269 creator A5074021977 @default.
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- W1832558269 date "2012-06-05" @default.
- W1832558269 modified "2023-09-26" @default.
- W1832558269 title "Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior" @default.
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- W1832558269 doi "https://doi.org/10.48550/arxiv.1206.1088" @default.
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