Minimum Description Length (MDL) inference is based on the intuition that understanding the available data can be defined in terms of the ability to compress the data, i.e. to describe it in full using a shorter representation. This brief introduction discusses the design of the various codes used to implement MDL, focusing on the philosophically intriguing concepts of luckiness and regret : a ...

Position specific score matrices (PSSMs) are derived from multiple sequence alignments to aid in the recognition of distant protein sequence relationships. The PSI-BLAST protein database search program derives the column scores of its PSSMs with the aid of pseudocounts, added to the observed amino acid counts in a multiple alignment column. In the absence of theory, the number of pseudocounts u...

Application of the Minimum Description Length principle to optimization queries in probabilistic programming was investigated on the example of the C++ probabilistic programming library under development. It was shown that incorporation of this criterion is essential for optimization queries to behave similarly to more common queries performing sampling in accordance with posterior distribution...

We present The Cruncher, a simple representation framework and algorithm based on minimum description length for automatically forming an ontology of concepts from attribute-value data sets. Although unsupervised, when The Cruncher is applied to an animal data set, it produces a nearly zoologically accurate categorization. We demonstrate The Cruncher’s utility for finding useful macro-actions i...

Bayesian networks are parametric models for multidimensional domains exhibiting complex dependencies between the dimensions (domain variables). A central problem in learning such models is how to regularize the number of parameters; in other words, how to determine which dependencies are significant and which are not. The normalized maximum likelihood (NML) distribution or code offers an inform...

The Minimum Description Length (MDL) approach to shape modelling seeks a compact description of a set of shapes in terms of the coordinates of marks on the shapes. It has been shown that the mark positions resulting from this optimisation to a large extent solve the so-called point correspondence problem: How to select points on shapes defined as curves so that the points correspond across a da...

The minimum description length (MDL) principle originated from data compression literature and has been considered for deriving statistical model selection procedures. Most of the existing methods that use the MDL principle focus on models with independent data, particularly in the context of linear regression. This paper considers data with repeated measurements and studies the selection of fi...

Ever since the discovery of a molecular clock, many methods have been developed to reconstruct the molecular evolutionary phylogenetic trees. In this paper, we deal with this problem from the viewpoint of an inductive inference and apply Rissanen’s minimum description length principle to extract the minimum complexsty phylogenetic tree. OUT method describes the complexity of molecular phylogene...

Multinomial processing tree (MPT) modeling has been widely and successfully applied as a statistical methodology for measuring hypothesized latent cognitive processes in selected experimental paradigms. In this article, we address the problem of selecting the best MPT model from a set of scientifically plausible MPT models, given observed data. We introduce a minimum description length (MDL) ba...

We describe a new abstract model for the computational learning of grammars. The model deals with a learning process in which an algorithm is given an input of a large set of training sentences that belong to some unknown grammar. The algorithm then tries to infer this grammar. Our model is based on the well-known Minimum Description Length Principle. It is quite close to, but more general than...