This paper is a review of a particular approach to the method of maximum entropy as a
general framework for inference. The discussion emphasizes pragmatic elements in the …

Entropic dynamics is a framework in which the laws of dynamics are derived as an
application of entropic methods of inference. Its successes include the derivation of quantum …

In the context of irreversible dynamics, associating to a physical process its intuitive reverse
can result to be a quite ambiguous task. It is a standard choice to define the reverse process …

In a recent paper (Entropy 2020, 22 (1), 17), Tsallis states that entropy—as in Shannon or
Kullback–Leiber's definitions—is inadequate to interpret black hole entropy and suggests …

It is generally assumed that the Rényi entropy is maximized by the q-exponentials and is
hence useful to construct a generalized statistical mechanics. However, to the best of our …

This article expands the framework of Bayesian inference and provides direct probabilistic
methods for approaching inference tasks that are typically handled with information theory …

Here we present the entropic dynamics formalism for networks. That is, a framework for the
dynamics of graphs meant to represent a network derived from the principle of maximum …

Using first principles from inference, we design a set of functionals for the purposes of
ranking joint probability distributions with respect to their correlations. Starting with a general …

Entropy and relative entropy measures play a crucial role in mathematical information
theory. The relative entropies are also widely used in statistics under the name of …

Abstract The recent article'Entropic Updating of Probability and Density Matrices'[1] derives
and demonstrates the inferential origins of both the standard and quantum relative entropies …