Enhancing the Predictive Performance of BayesianGraphical

By David Madigan, Jonathan Gavrin, Adrian E. Raftery, Richard Chapman, Denise Draper, Peter J. Dunbar. 1995

Subfields: Probabilistic & Bayesian Methods

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In Probabilistic & Bayesian Methods

1. Yoshua Bengio
2. Stefan Bauer
3. Qiang Shen
4. Stuart Aitken
5. Rónán Daly
6. Simon Lacoste-Julien
7. Tristan Deleu
8. Kevin P. Murphy

Citations in the corpus

• Bayesian Structure Learning with Generative Flow Networks (2022, methodology): “Given a dataset of observations, most of the existing algorithms for structure learning of Bayesian networks (Pearl, 1988) return a single DAG (or a single equivalence class; Chickering, 2002), and in practice those may lead to poorly calibrated predictions (Madigan et al., 1994).”
• Bayesian structure learning using dynamic programming and MCMC (2007, methodology): “e can compute the optimal tree structure in O (d 2 log d) time [CL68, MJ00]. It can be proven that averaging over the uncertainty in G will, on average, produce higher test-set predictive likelihoods [MGR95]. The DP algorithm can compute the marginal likelihood of the data, p( D ) (marginalizing ove”
• Learning Bayesian networks: approaches and issues (2011, background): “One of these forms to specify a prior distribution uses ‘imaginary data’, elicited from a domain expert as shown by Madigan et al. (1994).”