Sentence examples for maximum information entropy from inspiring English sources
Maximum information entropy principle.
In particular, we wish to indicate some challenges and expound our recent results on the maximum information entropy approach to the analysis of stochastic dynamical systems.
Monavari et al. (2016) proposed using the Maximum Information Entropy Principle (MIEP) to derive closure approximations for infinite hierarchy of CDD evolution equations.
By using the method of Lagrange multipliers, we can construct a DODF which has maximum information entropy and is consistent with the known alignment tensors.
The knowledge about the influencing quantities and parameters is expressed by their degree of belief, i.e. appropriate probability density functions that usually are obtained by utilizing the principle of maximum information entropy and the Bayes theorem.
The fundamental idea is to use the Maximum Information Entropy Principle (MIEP) in order to estimate the DODF based upon the information contained in alignment tensors up to order k, and then use the estimated DODF to evaluate, from Eq. (2), the missing alignment tensor ρ k+1).
On the basis of two well-pronounced drops in the maximum information entropies IEmax extracted from photon counts in a bubble column (0.162 m in ID) operated with an air therminol LT system, two transition velocities Utrans (0.02 and 0.09 m s−1) were identified at ambient pressure P.
The entropy functions are used to identify optimal trade-offs between the maximum possible information content and the minimum shared information among stations.
BWCPSO method puts the minimized kernel entropy components number with the maximum stage information of rolling bearing as its objective.
While the networks are optimized to have the maximum amount of information with minimum redundancy based on entropy concepts, this study extends the traditional entropy applications to the hydrometric network design by introducing several improvements.
We used a subspace GMM with maximum mutual information-based discriminative training (MMI-SGMM) [43] and a cross-entropy training DNN for the acoustic model.
