Sentence examples for mutual information computations from inspiring English sources

Nonlinear correlation analyses and mutual information computations led to characterize a complex network connecting ERalpha36 to either non-genomic estrogen signaling or to metastatic process.

Time complexity of mutual information computation is similar to information gain.

In order to make the mutual information computation in EXIT analysis, tractable using a Gaussian approximation[14], we propose a simple numerical approach based on Monte-Carlo simulations to fit a Gaussian pdf to outgoing LLRs from the SCF nodes.

One of the key issues addressed here is the mutual information computation (as required for EXIT analysis) for messages passed from factor nodes in the joint factor graph of the two LDPC codes, referred to as source channel factor (SCF) nodes, which represent the joint probabilities of the two sources and the output conditional probability density function (pdf) of the GMAC.

To facilitate the mutual information calculation, we formalize the definition of mutual information as follows.

We propose a novel method named sort and count for efficient parallelization of mutual information (MI) computation designed for massively multi-processing architectures.

Z-scored residual mutual information first computes a linear regression of mutual information against the product of the means of the positional mutual information distributions.

Mutual information was computed using a Gaussian Kernel estimate.

Finally, to simplify computations involving mutual information, each probe was binned into B equal frequency bins.

For reducing the computation complexity, mutual information (MI) theory is utilized to get the reduction feature set without compromising classification accuracy.

There are numerical problems associated with the computation of mutual information.