MDL Tutorial

author: Peter Grünwald, Centrum Wiskunde & Informatica (CWI)
published: Aug. 12, 2008,   recorded: July 2008,   views: 2023
Categories

Slides

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

We give a self-contained tutorial on the Minimum Description Length (MDL) approach to modeling, learning and prediction. We focus on the recent (post 1995) formulations of MDL, which can be quite different from the older methods that are often still called 'MDL' in the machine learning and UAI communities.

In its modern guise, MDL is based on the concept of a `universal model'. We explain this concept at length. We show that previous versions of MDL (based on so-called two-part codes), Bayesian model selection and predictive validation (a variation of cross-validation) can all be interpreted as approximations to model selection based on 'universal models'. Modern MDL prescribes the use of a certain `optimal' universal model, the so-called `normalized maximum likelihood model' or `Shtarkov distribution'. This is related to (yet different from) Bayesian model selection with non-informative priors. It leads to a penalization of `complex' models that can be given an intuitive differential-geometric interpretation. Roughly speaking, the complexity of a parametric model is directly related to the number of distinguishable probability distributions that it contains. We also discuss some recent extensions such as the 'luckiness principle', which can be used if the Shtarkov distribution is undefined, and the 'switch distribution', which allows for a resolution of the AIC-BIC dilemma.

See Also:

Download slides icon Download slides: icml08_grunwald_mld_01.pdf (237.6 KB)

Download slides icon Download slides: icml08_grunwald_mld_01.ppt (889.5 KB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: