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Compute a probability mass function from.
In this way, we can suitably separate the terms contained the mass function from the terms of the field derivatives.
In general, it is assumed that the q-level quantization admits the alphabet A q = {0, 1, …, q − 1} then, the probability mass function from m amples s1, …, s m is estimated as (8) Where, 1 is the indicator function, defined as (9) By substituting (9) in (1) entropies can be estimated and these entropy estimates can be substituted in (3) and (6) to obtain the MI and CMI estimates [ 11].
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An algorithm is proposed to compute transition probability matrix and holding time mass functions from the site navigation data.
The proposed input design technique optimizes a scalar cost function of the information matrix, by computing the optimal stationary probability mass function (pmf) from which an input sequence is sampled.
To start with, all the solutions, which are the dusts in the algorithm model, are initialized randomly, or based on the prior knowledge; what's more, we assign every dust (solution) a weight, we call it mass, whose values are based on the mass function generated from the space of the problem solution; finally, the GFA begins.
Discrete subjective probability mass functions for incomplete and fuzzy factors can be obtained from logic-tree and membership functions respectively.
To assess statistically the distribution of IS elements, we used a probability mass function (pmf) derived from the average number of transposases found per window for each genome.
A mass function m is a function from P to R + that fulfills the following conditions { m = 0 ∑ A ∈ P m ( A ) = 1 (9).
If one spectrum alone is observed, only a quantity called the mass function can be derived, from which is calculated a lower limit to the stellar masses.
The value of this sum is simply the hypergeometric probability mass function, and we sum from x up to the size of the protein family to estimate the probability that one species will have x or more proteins in this protein family.
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