Examples of using Non-negative in English and their translations into Russian
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Suppose again that N is also an independent, discrete random variable taking values on the non-negative integers, with probability generating function GN and probability density f i Pr{ N i}{\displaystyle f_{ i}=\ Pr\{ N= i\.
Importantly the non-negative definiteness of this function enables its spectral decomposition using the Karhunen-Loève expansion.
If the quadratic form takes only non-negative(respectively only non-positive)
The exception is the non-negative monomial matrices:
Recall that a density operator is a non-negative operator on a Hilbert space with unit trace.
and H~{\displaystyle\mathbf{\tilde{H}}} applies at least if B is a non-negative monomial matrix.
Note that there are several other equivalent definitions of being positive semidefinite, for example, positive semidefinite matrices are self-adjoint matrices that have only non-negative eigenvalues.
in which each vertex has a non-negative cost.
is said to be a mean if Λ has norm 1 and is non-negative, i.e. f≥ 0 a.e. implies Λ(f)≥ 0.
It was first used by Sharafutdinov to show that any two souls of a complete Riemannian manifold with non-negative sectional curvature are isometric.
making sure that wind speed stays non-negative.
which is created by non-negative matrix factorization.
In astronomy, NMF is a promising method for dimension reduction in the sense that astrophysical signals are non-negative.
both must be non-negative.
In comparison with PCA, NMF does not remove the mean of the matrices which leads to unphysical non-negative fluxes, therefore NMF is able to preserve more information than PCA as demonstrated by Ren et al. Principal component analysis can be employed in a nonlinear way by means of the kernel trick.
the len value is a non-negative number, this function returns a string that is a substring of the str value,
Non-negative matrix factorization(NMF or NNMF), also non-negative matrix approximation is a group of algorithms in multivariate analysis
the resulting problem may be called non-negative sparse coding due to the similarity to the sparse coding problem,
It became more widely known as non-negative matrix factorization after Lee
In weighted complete graphs with non-negative edge weights, the weighted longest path problem is the same as