Sentence examples for relative entropy between from inspiring English sources

which is the relative entropy between the joint distribution,, and the product of marginal distributions and.

We are able to control the relative entropy between the N-particle distribution and the expected limit which solves the corresponding Vlasov system.

By definition, the mutual information of two random variables and with joint probability density function (pdf) and marginal pdfs and is the relative entropy between the joint distribution and the product distribution, that is, (14).

Thus the amount of information can be measured in a natural way by the distance (relative entropy) between the prior and posterior distributions of the observer over the available space of hypotheses.

It is worth noting that S 0 is a constant related to the differential entropy and is obtained as the limit (as ℓ→0) of the relative entropy between the distribution of occurrences of hypercubes over the attractor and that of the uniform occupation of the hypercubes [37, 38].

With the Gaussian random vector r, the relative entropy between p 1=Pr(r|x 1) and p 2=Pr(r|x 2) has the following closed-form expression [56] Dleft(p_{1} || p_{2}right) = frac{1}{2} left[ log frac{|mathbf{C}_{2}|}{|mathbf{C}_{1}|} + text{tr} left(mathbf{C}_{2}^{-1} mathbf{C}_{1}right) - M right], (3).

As it can be seen in Table 2, the relative entropies between the model-generated mixed density functions and density functions estimated from experimental data were reduced significantly, particularly for Model M1.

Figure 5 and Additional file 7 compare relative entropies between the native and the synonymous-codon-shuffled dataset.

Finally, the leftmost distribution in the upper panels of Figure 1 corresponds to the relative entropy Ds between the original and shuffled texts in each language.

In many settings, an m/ z window selected for dissociation has multiple coeluting ion species and a direct relative entropy distance between a library spectrum and a data spectrum with chemical noise present can fail to detect(19) because overlapping chemical noise can result in a high entropy cost.

To optimize the mean-field approximation, each variable update needs to reduce the relative entropy (KL-divergence) between the estimated and the true distribution [15].