Parametric complexity is a central concept in Minimum Description Length (MDL) model selection. In practice it often turns out to be infinite, even for quite simple models such as the Poisson and Geometric families. In such cases, MDL model selection as based on NML and Bayesian inference based on Jeffreys’ prior can not be used. Several ways to resolve this problem have been proposed. We condu...

Parametric complexity is a central concept in MDL model selection. In practice it often turns out to be infinite, even for quite simple models such as the Poisson and Geometric families. In such cases, MDL model selection as based on NML and Bayesian inference based on Jeffreys’ prior can not be used. Several ways to resolve this problem have been proposed. We conduct experiments to compare and...

The Minimum Description Length (MDL) principle is an information theoretic approach to inductive inference that originated in algorithmic coding theory. In this approach, data are viewed as codes to be compressed by the model. From this perspective, models are compared on their ability to compress a data set by extracting useful information in the data apart from random noise. The goal of model...

Background Diffuse intrinsic pontine gliomas (DIPG’s) are immunologically inert tumors with a median survival of 9–15 months. Radiation therapy (RT) is the mainstay treatment for DIPG but associated immunodepletion tumor microenvironment (TME) at high dose ranges. FLASH, or ultra-fast rate RT, represents novel ablative technique that may spare TME immune responses while decreasing burden. Here,...

The Minimum Description Length (MDL) principle is a general, well-founded theoretical formalization of statistical modeling. The most important notion of MDL is the stochastic complexity, which can be interpreted as the shortest description length of a given sample of data relative to a model class. The exact definition of the stochastic complexity has gone through several evolutionary steps. T...

Nano Magnetic Logic (NML) has been attracting application in optical computing, nanodevice formation, and low power. In this paper nanoscale architecture such as the decoder, multiplexer, and comparator are implemented on perpendicular-nano magnetic logic (pNML) technology. All these architectures with the superiority of minimum complexity and minimum delay are pointed. The proposed architectur...

In this talk, we discuss the application of the normalized maximum likelihood (NML) for model selection in Gaussian linear regression. All the results which will be presented have been recently published in [1].

The Minimum Description Length (MDL) is an informationtheoretic principle that can be used for model selection and other statistical inference tasks. One way to implement this principle in practice is to compute the Normalized Maximum Likelihood (NML) distribution for a given parametric model class. Unfortunately this is a computationally infeasible task for many model classes of practical impo...

We regard histogram density estimation as a model selection problem. Our approach is based on the information-theoretic minimum description length (MDL) principle, which can be applied for tasks such as data clustering, density estimation, image denoising and model selection in general. MDLbased model selection is formalized via the normalized maximum likelihood (NML) distribution, which has se...