The principle of maximum entropy provides a useful method for inferring statistical mechanics models from observations in correlated systems, and is widely used in a variety of fields where accurate data are available. While the assumptions underlying maximum entropy are intuitive and appealing, its adequacy for describing complex empirical data has been little studied in comparison to alternat...

I present empirical comparisons between a standard statistical translation model and an equivalent Maximum Entropy model. Results show that the Maximum Entropy model is promising, but highly sensitive to the method of feature selection.

The channel representation allows the construction of soft histograms, where peaks can be detected with a much higher accuracy than in regular hard-binned histograms. This is critical in e.g. reducing the number of bins of generalized Hough transform methods. When applying the maximum entropy method to the channel representation, a minimum-information reconstruction of the underlying continuous...

I present empirical comparisons between a standard statistical translation model and an equivalent Maximum Entropy model. Results show that the Maximum Entropy model is promising, but highly sensitive to the method of feature selection.

ConIII (pronounced CON-ee) is an open-source Python project providing a simple interface to solving maximum entropy models, with a focus on the Ising model. We describe the maximum entropy problem and give an overview of the algorithms that are implemented as part of ConIII (https://github.com/bcdaniels/coniii) including Monte Carlo histogram, pseudolikelihood, minimum probability flow, a regul...

Maximum Entropy Method (MEM) for the reconstruction of sign{ altering functions from two dimensional tomographic measurement data is developed. Three-dimensional algorithm for parallel beam geometry are considered. Results of numerical simulations for composite model are presented. keywords: Tomography, Maximum Entropy Method, Ill-posed problems

We develop a complete convergence theory for the Maximum Entropy method based on moment matching for a sequence of approximate statistical moments estimated by the Multilevel Monte Carlo method. Under appropriate regularity assumptions on the target probability density function, the proposed method is superior to the Maximum Entropy method with moments estimated by the Monte Carlo method. New t...

We propose an information theoretic approach to approximating asymptotic distributions of statistics using the maximum entropy densities. Conventional maximum entropy densities are typically defined on a bounded support. For distributions defined on unbounded supports, we propose to use an asymptotically negligible dampening function for the maximum entropy approximation such that it is well de...