Sentence examples for mutual information process from inspiring English sources

We figure out the semantic type probability p(x) and the relation predicate probability p y) from the data set, using them in the mutual information process.

We describe the algorithm we have developed based on the technique of determinant inequalities and the concept of minimization of mutual information to process the covariance matrix element by element to minimize the noise caused by systematic uncertainty in the SPECT imaging of 113mIn and its utility to experimentalists to design and improve their process of measurement and instrumentation.

Finally, characterizing and representing EXIT curves using mutual information help stabilize the process and alleviate the inaccuracy caused by Gaussian approximation and other factors.

Leakage signals are decomposed into mode components by VMD, and an adaptive selection method using mutual information is proposed to process these mode components and obtain the sensitive components closely related to the leak.

In this paper we present a new method to improve rock classification using digital image analysis, feature selection based on mutual information and a voting process to take into account boundary information.

It describes the exchange of the mutual information in the iterative process in order to determine the required number of iterations, the convergence threshold, and the average decoding trajectory.

Compared with the previously used method of density evolution, the EXIT chart is computationally simplified, and it also allows to visualize the evolution of mutual information through iterative decoding process in a graph.

We combine the variance function of the Gaussian process and the mutual information to design an informative and noise-robust planning algorithm for multiple robots.

Thus in contrast to lagged mutual information, the past of the process X is conditioned out.

In fact, for a single inhomogeneous Poisson process, the mutual information and its lower bound coincide in leading-order of the signal amplitude [53].

Our solution to this issue was to regularize the Bayesian learning process using mutual information between datasets as a prior to upweight or downweight the total possible contribution of each dataset.