23 papers · ranked by Valyu relevance
Joshua Goodman
We show that maximum entropy (maxent) models can be modeled with certain kinds of HMMs, allowing us to construct maxent models with hidden variables, hidden state sequences, or other characteristics. The models can be trained using the forwardbackward algorithm. While the results are primarily of theoretical interest…
Orestis Loukas, Ho‐Ryun Chung
In most data-scientific approaches, the principle of Maximum Entropy (maxent) is used to a posteriori justify some parametric model which has been already chosen based on experience, prior knowledge or computational simplicity. In a perpendicular formulation to conventional model building, we start from the linear…
Xiao Feng, Daniel S. Park, Ye Liang, Ranjit Pandey + 1 more
Ecological niche models are widely used in ecology and biogeography. Maxent is one of the most frequently used niche modeling tools, and many studies have aimed to optimize its performance. However, scholars have conflicting views on the treatment of predictor collinearity in Maxent modeling. Despite this lack of…
Mona Nazeri, Kamaruzaman Jusoff, Nima Madani, Ahmad Rodzi Mahmud + 3 more
'Abdul Rani Bahman' 'Lalit Kumar' 'Sean Walker'] One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum…
Julien Vollering, Rune Halvorsen, Sabrina Mazzoni
The widely used “Maxent” software for modeling species distributions from presence-only data (Phillips et al., Ecological Modelling, 190, 2006, 231) tends to produce models with high-predictive performance but low-ecological interpretability, and implications of Maxent's statistical approach to variable transformation…
Gabriel P. Langlois, Jatan Buch, Jérôme Darbon
Maximum entropy (Maxent) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, Maxent models need efficient optimization algorithms to scale well for big data applications. Stateof-the-art algorithms for…
Ondřej Mikula
Environmental niche modelling (ENM) uses different types of variables to predict species occurrence. In widespread use are variables derived from climatic curves, i.e., average annual changes in some climatic parameter. This study shows how to use the climatic curves themselves as ENM predictors. The key step is…
Christopher Wang, Elianna Schimke, Tristan Kako, Aiden Gao + 3 more
Biological organisms have sensors that communicate information about the environment. Analyzing how well these biological sensors function has usually been done with mutual information between the sensor signal and the environment, but that can be computationally intractable and summarize something quite complex with…
A. E. Allahverdyan
Maximum entropy (MAXENT) method has a large number of applications in theoretical and applied machine learning, since it provides a convenient non-parametric tool for estimating unknown probabilities. The method is a major contribution of statistical physics to probabilistic inference. However, a systematic approach…
Narkis S. Morales, Ignacio C. Fernándezb, Victoria Baca-Gonzálezd
Environmental niche modeling (ENM) is commonly used to develop probabilistic maps of species distribution. Among available ENM techniques, MaxEnt has become one of the most popular tools for modeling species distribution, with hundreds of peer-reviewed articles published each year. MaxEnt’s popularity is mainly due to…
John L. Schnase, Mark L. Carroll, Roger L. Gill, Glenn S. Tamkin + 4 more
MaxEnt is an important aid in understanding the influence of climate change on species distributions and abundance. There is growing interest in using IPCC-class global climate model outputs as environmental predictors in this work. These models provide realistic, global representations of the climate system…
Rainier Barrett, Mehrad Ansari, Gourab Ghoshal, Andrew Dickson White
Inferring the input parameters of simulators from observations is a crucial challenge with applications from epidemiology to molecular dynamics. Here we show a simple approach in the regime of sparse data and approximately correct models, which is common when trying to use an existing model to infer latent variables…
John L. Schnase, Mark L. Carroll, Roger L. Gill, Glenn S. Tamkin + 6 more
'Jian Li' 'Savannah L. Strong' 'Thomas P. Maxwell' 'Mary E. Aronne' 'Caleb S. Spradlin' 'Yangyang Xu'] MaxEnt is an important aid in understanding the influence of climate change on species distributions. There is growing interest in using IPCC-class global climate model outputs as environmental predictors in this…
Abbas Naqibzadeh, Jalil Sarhangzadeh, Ahad Sotoudeh, Marjan Mashkur + 1 more
Habitat suitability models are useful tools for a variety of wildlife management objectives. Distributions of wildlife species can be predicted for geographical areas that have not been extensively surveyed. The basis of these models’ work is to minimize the relationship between species distribution and biotic and…
Narkis S. Morales, Ignacio C. Fernández
MaxEnt is a popular maximum entropy-based algorithm originally developed for modelling species distribution, but increasingly used for land-cover classification. In this article, we used MaxEnt as a single-class land-cover classification and explored if recommended procedures for generating high-quality species…
Mark Rademaker, Laurens Hogeweg, Rutger Vos
Knowledge of global biodiversity remains limited by geographic and taxonomic sampling biases. The scarcity of species data restricts our understanding of the underlying environmental factors shaping distributions, and the ability to draw comparisons among species. Species distribution models (SDMs) were developed in…
Yu Zhang, Mitchell Bucklew
In this paper, we introduce Max Markov Chain (MMC), a novel representation for a useful subset of High-order Markov Chains (HMCs) with sparse correlations among the states. MMC is parsimony while retaining the expressiveness of HMCs. Even though parameter optimization is generally intractable as with HMC approximate…
David P. Carcamo, Nicholas J. Weaver, Purushottam D. Dixit, Christopher W. Lynn
'Christopher W. Lynn'] When constructing models of the world, we aim for optimal compressions: models that include as few details as possible while remaining as accurate as possible. But which details—or features measured in data—should we choose to include in a model? Here, using the minimum description length…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Woo-Young Ahn, Nathaniel Haines, Lei Zhang
Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Theresa Ramelot, Roberto Tejero, Gaetano Montelione
Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…