Ill-posed inverse problems of the form y = Xp where y is J-dimensional vector of a data, p is m-dimensional probability vector which can not be measured directly and matrix X of observable variables is a known J ×m matrix, J < m, are frequently solved by Shannon’s entropy maximization (MaxEnt, ME). Several axiomatizations were proposed (see for instance [1], [2], [3], [4], [5], [6], [7], [8], a...

Most three dimensional constitutive relations that have been developed to describe the behavior of bodies are correlated against one dimensional and two dimensional experiments. What is usually lost sight of is the fact that infinity of such three dimensional models may be able to explain these experiments that are lower dimensional. Recently, the notion of maximization of the rate of entropy p...

We present an approach to bounded constraintrelaxation for entropy maximization that corresponds to using a double-exponential prior or `1 regularizer in likelihood maximization for log-linear models. We show that a combined incremental feature selection and regularization method can be established for maximum entropy modeling by a natural incorporation of the regularizer into gradientbased fea...

Entropic priors, recently revisited within the context of theoretical physics, were originally introduced for image processing and for general statistical inference. Entropic priors seem to represent a very promising approach to “objective” prior determination when such information is not available. The attention has been mostly limited to continuous parameter spaces and our focus in this work ...

We show that the correct entropy, temperature (and absorption probability) of non-extremal black p-brane can be reproduced by a certain thermodynamical model when maximizing its entropy. We argue the relation of the model and the geometrical similarity of black p-brane and R AdS black hole at near horizon region. We also argue the relation of the maximization of the entropy and a saddle point a...

A GIMP Retinex filtering can be used for enhancing images, with good results on foggy images, as recently discussed. Since this filter has some parameters that can be adjusted to optimize the output image, several approaches can be decided according to desired results. Here, as a criterion for optimizing the filtering parameters, we consider the maximization of the image entropy. We use, beside...

Persistent misconceptions existing for dozens of years and influencing progress in various fields of science are sometimes encountered in the scientific and especially, the popular-science literature. The present brief review deals with two such interrelated misconceptions (misunderstandings). The first misunderstanding: entropy is a measure of disorder. This is an old and very common opinion. ...

Semi-continuous data comes from a distribution that is mixture of the point mass at zero and continuous with support on positive real line. Such appear in several real-life situations like blind signal separation problems, modeling loss finance insurance, sales consumer goods, daily precipitation data, to name few. In this paper, we present novel algorithm estimate density function for semi-con...

In the absence of assigned tasks, a learning agent typically seeks to explore its environment efficiently. However, pursuit exploration will bring more safety risks. An under-explored aspect reinforcement is how achieve safe efficient when task unknown. this paper, we propose practical Constrained Entropy Maximization (CEM) algorithm solve task-agnostic problems, which naturally require finite ...