Bayes Factors, relations to Minimum Description Length, and overlapping model classes

Bayes Factors , Relations to Minimum Description Length , and Overlapping Model Classes

This article presents a non-technical perspective on two prominent methods for analyzing experimental data in order to select among model classes. Each class consists of model instances; each instance predicts a unique distribution of data outcomes. One method is Bayesian Model Selection (BMS), instantiated with the Bayes factor. The other is based on the Minimum Description Length principle (M...

Model Selection Based on Minimum Description Length.

We introduce the minimum description length (MDL) principle, a general principle for inductive inference based on the idea that regularities (laws) underlying data can always be used to compress data. We introduce the fundamental concept of MDL, called the stochastic complexity, and we show how it can be used for model selection. We briefly compare MDL-based model selection to other approaches ...

Minimum Description Length Criterion

he intelligibility of speech in communication systems is generally reduced by interfering noise. This interference, which can take the form of environmental noise, reverberation, competing speech, or electronic channel noise, reduces intelligibility by masking the signal of interest. The reduction in intelligibility is particularly troublesome for listeners with hearing impairments, who have gr...

Minimum Description Length Principle

The minimum description length (MDL) principle states that one should prefer the model that yields the shortest description of the data when the complexity of the model itself is also accounted for. MDL provides a versatile approach to statistical modeling. It is applicable to model selection and regularization. Modern versions of MDL lead to robust methods that are well suited for choosing an ...

The Minimum Description Length