
Applications of the MDL Principle to Prediction and Model Selection and Testing
 Duration: 1.1.2009 – 31.12.2012
 Funding: Academy of Finland
 Project leader: Professor Petri Myllymäki
 Keywords: MDL, normalized maximum likelihood, universal models, variable order Markov chains, model selection, model testing
Abstract
The main application areas of statistical inference are model
selection, hypothesis testing, and prediction. In model selection the
goal typically is to increase our understanding of a problem area, by
utilization of data analysis, data mining, and information extraction
tools, and in hypothesis testing we estimate the validity of a certain
hypothesis about the problem. In prediction, the task, of course, is
to estimate the probability of some unknown quantity, which typically
is temporally located in the future. To perform these statistical
inference/machine learning tasks we need a theoretically solid
framework, which is logically correct satisfying certain reasonable
optimality criteria, while at the same time providing computationally
feasible methods for practical applications. Information theory offers
an excellent foundation for such a framework.
 Fig. 1. Illustration of different
normalization methods in universal models: a) sequential
normalization (left), and b) factorized sequential
normalization (right). Source:
[2]. 

Our earlier pioneering work on informationtheoretic statistical
inference, in which the Minimum Description Length (MDL) principle
plays a central role, has recently spurred an influx of new ideas,
problems, and extensions. We believe that the new ideas lead to
theoretically and practically significant advances in MDLbased
modeling. The goal of the project is to study these issues further,
focusing on four research areas: sequentially normalized universal
models, optimally distinguishable models, extensions of the structure
function, and nonstationary modeling. In addition to theoretical
advances in these areas, we will develop new algorithms suitable for
practical model selection, testing, and prediction tasks, and
empirically demonstrate their validity using both artificial and
realworld data sets from various domains.
Publications
 J. Rissanen, Model Selection and Testing by the MDL Principle. Chapter 2 in F. EmmertStreib and M. Dehmer, eds., Information Theory and Statistical Learning, Springer, 2009.
 T. Silander, T. Roos, and P. Myllymäki, Locally Minimax Optimal Predictive Modeling with Bayesian Networks. To appear in Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS 2009), Clearwater Beach, FL, USA.
 T. Roos and B. Yu, Sparse Markov Source Estimation via Transformed Lasso, IEEE Information Theory Workshop (ITW09), Volos, Greece, June 10–12, 2009.
 T. Roos and B. Yu, Estimating Sparse Models from Multivariate Discrete Data via Transformed Lasso, Information Theory and Applications Workshop (ITA09), San Diego CA, USA, February 8–13, 2009.
 T. Roos, P. Myllymäki, and J. Rissanen, MDL Denoising Revisited,
IEEE Trans. Signal Processing, 57:9, 3347–3360, 2009.
 T. Silander, T. Roos, and P. Myllymäki, Learning locally minimax
optimal Bayesian networks,
International Journal of Approximate Reasoning
(Special Issue on Selected Papers from PGM08), 51:5, 544–557,
2010.
 J. Rissanen, T. Roos, and P. Myllymäki, Model Selection by
Sequentially Normalized Least Squares,
Journal of Multivariate Analysis, 101:4, 839–849, 2010.
 J. Rissanen and P. Myllymäki, MDL Interval Estimation,
Information Theory and Applications Workshop (ITA10),
San Diego, CA, USA, February 1–5, 2010.
 J. Rissanen, The MDL Principle, in C. Sammut and G.I. Webb (editors),
Encyclopedia of Machine Learning, Springer, 2010.
 D.F. Schmidt and T. Roos, On the consistency of sequentially
normalized least squares, to appear in Proc. 3rd Workshop on
Information Theoretic Methods in Science and Engineering
(WITMSE10), 2010.
 A. Carvalho, T. Roos, A. Oliveira, and
P. Myllymäki, (2011). Discriminative learning of Bayesian networks
via factorized conditional loglikelihood, Journal of Machine
Learning Research
12(Jul):2181–2210.
 