Maximum entropy approach for modeling random uncertainties in transient elastodynamics

Maximum entropy approach for modeling random uncertainties in transient elastodynamics.

A new approach is presented for analyzing random uncertainties in dynamical systems. This approach consists of modeling random uncertainties by a nonparametric model allowing transient responses of mechanical systems submitted to impulsive loads to be predicted in the context of linear structural dynamics. The information used does not require the description of the local parameters of the mech...

A maximum entropy approach for property prediction of random microstructures

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...

Latent Maximum Entropy Approach for Semantic N-gram Language Modeling

A Maximum Entropy Approach for Semantic Language Modeling

The conventional n-gram language model exploits only the immediate context of historical words without exploring long-distance semantic information. In this paper, we present a new information source extracted from latent semantic analysis (LSA) and adopt the maximum entropy (ME) principle to integrate it into an n-gram language model. With the ME approach, each information source serves as a s...

Maximum Entropy Modeling Toolkit

The Maximum Entropy Modeling Toolkit supports parameter estimation and prediction for statistical language models in the maximum entropy framework. The maximum entropy framework provides a constructive method for obtaining the unique conditional distribution p*(y|x) that satisfies a set of linear constraints and maximizes the conditional entropy H(p|f) with respect to the empirical distribution...