Sentence examples for divergence of the probability from inspiring English sources
To visualize the divergence of the probability density functions of three mechanisms, we plotted the density curves of the first duplication time for nonfunctionalization, neofunctionalization, and subfunctionalization (Fig. 1b) with the values of parameters in Table 1.
The f-divergence of the probability measures μ 1 and μ 2 is defined as follows: Γ f ( μ 1, μ 2 ) = ∫ X p 2 ( x ) f ( p 1 ( x ) p 2 ( x ) ) λ ( d x ), where the function f is named the base function.
The divergence between the probability distribution of mRNA at 50s and at 33s (of simulated time) is plotted in Figure 3 in order to demonstrate that the simulation has reached a stationary state.
A stego process is thus defined as -secure if the Kullback-Leibler divergence between the probability density functions of the cover document and those of this very same content embedding a message (i.e., stego) is less than : (1).
At first, the threshold value for a noisy speech frame is analytically obtained by solving equations either based on the SKL divergence between the probability distribution functions (pdfs) of the t k, m j of the noisy speech and that of the noise or based on the SKL divergence between the pdfs of the t k, m j of the noisy speech and that of the clean speech.
In the case of divergence-restricted LGT, the probability of success for a proposed transfer decreased linearly with increasing number of iterations separating two genomes (i.e., # of iterations since their last common ancestor) up to 500, and LGT forbidden between genomes whose common ancestor was > 500 generations in the past.
A higher Kullback Leibler (KL) value indicates more divergence of the root state posterior probability from its prior [ 13].
Thus, if the method has the desirable behavior such that the estimated posterior probability of the one-divergence model is an unbiased estimate of the probability that the one-divergence model is correct, the points should fall near the identity line.
This divergence can be decomposed into the entropy of the probability over final outcomes and an expected utility term.
Thus, given an image segment, we can express the heterogeneity of an image using the JS-divergence applied to the probability distribution of each segment.
