The analysis demonstrated the existence of piecewise-constant approximations to the input-vector probability density function, which is optimal for the proposed geometric piecewise-uniform vector quantizer.
This approach might be interpreted as basing the scores on the difference between inheritance vector-probabilities under the null and alternative hypothesis in all cases.
The result is a vector of probability scores for each candidate block border SNP.
P w = (P w 1... P w n ) represents the vector of probability weights given to samples.
If X is a random vector with probability density function, f(x), its differential entropy is defined by (1) H f = − ∫ f x ln f x d x.
Thus, given m, n and the vector of probabilities p, we can determine the probability of observing k or more chimeras of the same type.
In this case, we let p1, p2,…, p N be the vector of probabilities for the database and r1, r2,…, r N the vector of probabilities for the queries.
can be interpreted as a vector of probabilities.
Since y∗ is a vector of probabilities, e ⊺ y ∗ = 1.
The new information state will be a vector of probabilities computed according to the above formula.
For this purpose, a vector of probabilities is generated as: {mathbf{P}} = [pp_{1},pp_{2}, ldots,pp_{{N_{{text{imp}}} }} ] (9).
