The principle of maximum entropy, developed more than six decades ago, provides a systematic approach to modeling inference, and data analysis grounded in the principles of information theory, ...

What is the Objective Function in MaxEntIRL? Maximizing the entropy of the distribution over paths subject to the feature constraints from observed data implies that we maximize the likelihood of the ...

Maximum Entropy modeling provides a framework for integrating information for classification from many heterogeneous information sources manning99foundations,berger96. ME probability models have been ...

However, machine learning methods may provide an invaluable guide. The maximum entropy (MaxEnt) principle (10) offers the least-biased model for the sequence distribution by maximizing information ...

Remote sensing inversion problem is always ill-posed. However, regularization aims at turning the ill-posed problems into certainty. In this paper, taking the linear kernel-driven model as an example, ...

This paper proposes entropy balancing, a data preprocessing method to achieve covariate balance in observational studies with binary treatments. Entropy balancing relies on a maximum entropy ...

We have developed a mathematical model for the kinetics of labeled protein synthesis and degradation during bulk pulse–chase experiments. To enable fitting of this model to experimental data, we also ...

Nader Ebrahimi, The Maximum Entropy Method for Lifetime Distributions, Sankhyā: The Indian Journal of Statistics, Series A (1961-2002), Vol. 62, No. 2 (Jun., 2000 ...