The development of species distribution models (SDMs) can help conservation efforts by generating potential distributions and identifying areas of high environmental suitability for protection. Our study presents a distribution and habitat map for lowland tapir in South America. We also describe the potential habitat suitability of various geographical regions and habitat loss, inside and outsi...

The task of reconstruction of microstructures from their limited description is posed as a maximum entropy (MaxEnt) problem. Microstructural descriptors are taken in the form of volume fractions, correlation functions and grain sizes. Morphological and size quantifications are used as features of microstructure and samples consistent with these features are reconstructed. The nonuniqueness of t...

In this study, we tested the Maximum Entropy model (Maxent) for its application and performance in remotely sensing invasive Tamarix sp. Six Landsat 7 ETM+ satellite scenes and a suite of vegetation indices at different times of the growing season were selected for our study area along the Arkansas River in Colorado. Satellite scenes were selected for April, May, June, August, September, and Oc...

Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whet...

In addition to being used as a tool for ecological understanding, management and conservation of migratory waterfowl rely heavily on distribution models; yet these models have poor accuracy when compared to models of other bird groups. The goal of this study is to offer methods to enhance our ability to accurately model the spatial distributions of six migratory waterfowl species. This goal is ...

Maximum entropy inverse optimal control (MaxEnt IOC) is an effective means of discovering the underlying cost function of demonstrated human activity and can be used to predict human behavior over low-dimensional state spaces (i.e., forecasting of 2D trajectories). To enable inference in very large state spaces, we introduce an approximate MaxEnt IOC procedure to address the fundamental computa...

Prepositional phrases are a common source of ambiguity in natural language and many approaches have been devised to resolve this ambiguity automatically. In particular, several different machine learning approaches have now reached accuracy rates of around 84.5% on the benchmark dataset. Maximum entropy (maxent) models, despite their successful application in many other areas of natural languag...

In this paper, we present a maximum entropy (maxent) approach to the fusion of experts opinions, or classifiers outputs, problem. The maxent approach is quite versatile and allows us to express in a clear, rigorous, way the a priori knowledge that is available on the problem. For instance, our knowledge about the reliability of the experts and the correlations between these experts can be easil...