We present and relate recent results in prediction based on countable classes of either probability (semi-)distributions or base predictors. Learning by Bayes, MDL, and stochastic model selection will be considered as instances of the first category. In particular, we will show how analog assertions to Solomonoff’s universal induction result can be obtained for MDL and stochastic model selectio...

Application of the Minimum Description Length (MDL) principle to multiresolution image denoising has been somewhat unsuccessful to date. This disappointing performance is due to the crudeness of the underlying prior image models, which lead to overly sparse solutions. We propose a new family of complexity priors based on Rissanen's universal prior for integers , which produces estimates with be...

Incubation of the acutely dissected rat hippocampal slices in calcium-containing media resulted in spontaneous activation-translocation of classical PKC isoforms and their subsequent (especially gamma-type) proteolytic degradation. These changes were blocked by calpain inhibitor MDL 28 170 in 100 microM concentration. Rat hippocampal slices were metabolically prelabelled with 32Pi and stimulate...

We propose a new Successive State Splitting (SSS) algorithm based on the Minimum Description Length (MDL) criterion to design tied-state HMM topologies automatically. The SSS algorithm is a mechanism for creating both temporal and contextual variations based on the Maximum Likelihood (ML) criterion. However, it also needs to empirically predetermine control parameters for use as stop criteria, ...

We consider wavelet denoising based on minimum description length (MDL) principle. The derivation of an MDL denoising criterion proposed by Rissanen involves a renormalization whose effect on the resulting method has not been well understood so far. By inspecting the behavior of the method we obtain a characterization of its domain of applicability: good performance in the low variance regime b...

We apply minimum description length (MDL) principles to evaluate the merit of relaxing the rigidity of block models of haplotype structure. We accomplish this by developing an MDL formulation of the more general "haplotype motif" haplotype structure similar to an approach proposed independently by Koivisto et al. [K+04]. Comparison of equivalent block and motif MDL models on real and simulated ...

The MDL Principle (induction by compression) is applied with meticulous effort in the Krimp algorithm for the problem of itemset mining, where one seeks exceptionally frequent patterns in a binary dataset. As is the case with many algorithms in data mining, Krimp is not designed to cope with real-valued data, and it is not able to handle such data natively. Inspired by Krimp’s success at using ...

In this paper, we show how to accomodate a Bayesian variant of Rissanen’s MDL into on-line Bayesian adaptation to control both model structural complexity and parameterization complexity to best fit an available amount of adaptation data, the goal being minimization of resulting recognition error. An efficient bottom-up dynamic programming based pruning algorithm is developed for selecting mode...

The minimum description length (MDL) has for some decades been known to be an eecient tool for choosing model structure. We will in this contribution generalize MDL to adaptive algorithms in system and signal identiication. The parameter vector in these problems can either be considered as piecewise constant using seg-mentation and change detection algorithms or as time-varying estimated by rec...

Automatic construction of Shape Models from examples has been the focus of intense research during the last couple of years. These methods have proved to be useful for shape segmentation, tracking and shape understanding. In this paper novel theory to automate shape modelling is described. The theory is intrinsically defined for curves although curves are infinite dimensional objects. The theor...