288 citations · 485 across the 6 of their papers we have counts for
6 papers
Minimum Encoding Approaches for Predictive Modeling
Peter D Grunwald, Petri Kontkanen, Petri Myllymaki +2
We analyze differences between two information-theoretically motivated approaches to statistical inference and model selection: the Minimum Description Length (MDL) principle, and…
On Supervised Selection of Bayesian Networks
Petri Kontkanen, Petri Myllymaki, Tomi Silander +1
Given a set of possible models (e.g., Bayesian network structures) and a data sample, in the unsupervised model selection problem the task is to choose the most accurate model with…
Classifier Learning with Supervised Marginal Likelihood
Petri Kontkanen, Petri Myllymaki, Henry Tirri
It has been argued that in supervised classification tasks, in practice it may be more sensible to perform model selection with respect to some more focused model selection score,…
Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (2008)
David McAllester, Petri Myllymaki
This is the Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence, which was held in Helsinki, Finland, July 9 - 12 2008.
A simple approach for finding the globally optimal Bayesian network structure
Tomi Silander, Petri Myllymaki
We study the problem of learning the best Bayesian network structure with respect to a decomposable score such as BDe, BIC or AIC. This problem is known to be NP-hard, which means…
On Sensitivity of the MAP Bayesian Network Structure to the Equivalent Sample Size Parameter
Tomi Silander, Petri Kontkanen, Petri Myllymaki
BDeu marginal likelihood score is a popular model selection criterion for selecting a Bayesian network structure based on sample data. This non-informative scoring criterion assign…