Bayesian model learning based on predictive entropy
Journal of Logic, Language and Information 15 (1-2) (2006)
| Abstract | Bayesian paradigm has been widely acknowledged as a coherent approach to learning putative probability model structures from a finite class of candidate models. Bayesian learning is based on measuring the predictive ability of a model in terms of the corresponding marginal data distribution, which equals the expectation of the likelihood with respect to a prior distribution for model parameters. The main controversy related to this learning method stems from the necessity of specifying proper prior distributions for all unknown parameters of a model, which ensures a complete determination of the marginal data distribution. Even for commonly used models, subjective priors may be difficult to specify precisely, and therefore, several automated learning procedures have been suggested in the literature. Here we introduce a novel Bayesian learning method based on the predictive entropy of a probability model, that can combine both subjective and objective probabilistic assessment of uncertain quantities in putative models. It is shown that our approach can avoid some of the limitations of the earlier suggested objective Bayesian methods. | |||||||||
| Keywords | No keywords specified (fix it) | |||||||||
| Categories | ||||||||||
| Options |
|
|||||||||
Download options| PhilPapers Archive |
Upload a copy of this paper Check publisher's policy on self-archival Papers currently archived: 5,705 |
| External links |
 |
| Through your library | Configure |
Similar books and articlesEvan Heit (2001). What is the Probability of the Bayesian Model, Given the Data? Behavioral and Brain Sciences 24 (4):672-673.
Joel D. Velasco (2008). The Prior Probabilities of Phylogenetic Trees. Biology and Philosophy 23 (4):455-473.
Teddy Seidenfeld (1986). Entropy and Uncertainty. Philosophy of Science 53 (4):467-491.
Gert de Cooman & Peter Walley (2002). A Possibilistic Hierarchical Model for Behaviour Under Uncertainty. Theory and Decision 52 (4):327-374.
Frederick Eberhardt & David Danks (2011). Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. Minds and Machines 21 (3):389-410.
Analytics
Monthly downloads |
Added to index2009-01-28Total downloads4 ( #178,800 of 549,196 )Recent downloads (6 months)1 ( #63,397 of 549,196 )How can I increase my downloads? |
My notes
Discussion
