The DS theory is applied in various areas [3, 4, 24, 25], but, in image segmentation, the determination of mass functions is a hard task and the performance of the segmentation scheme is, however, largely conditioned by the appropriate determination of the mass functions.
Once the mass functions are estimated by the assumption of GD, the DS combination rule is applied to obtain the final segmented image.
In the discrete case the weights are given by the probability mass function, and in the continuous case the weights are given by the probability density function.
Note however that the divergence estimates made by the binomial mass function and the analysis with BEAST are conservative (i.e., the true time of the recombination event are probably more recent) given that we had only three sequences in the analysis.
(The mass distribution of newly formed stars is described empirically by the initial mass function).
The accuracy of the different proposed models is evaluated by comparing the probability mass function at each time scale and the queuing behavior (as assessed by the loss probability) corresponding to measured and synthetic traces generated from the inferred models.
In general, these equilibrium points are linearly stable when μ < μC; where μ is the mass ratio and μC is the critical mass function, defined by the parameters of the system.
Using the average number of elements per window, the probability of finding particular number of elements under a Poisson distribution was determined by using the probability mass function (pmf).
Let d n (i) represent the quantity of readers affected by i collision-set-n's and let X be the random number of collision-set-n, the discrete probability distribution of X is characterized by the following probability mass function: Pr ( X = i ) = d n ( i ) N, (30).
