Sentence examples for complete factorization from inspiring English sources

That approach was extended by Durrant and Mott (2010), who describe a partially Bayesian mixed model of QTL mapping: By focusing on additive effects of QTL for normally distributed traits with no additional covariates or population structure, they provided an efficient method for combined multiple imputation and shrinkage estimation via complete factorization of a pseudo-posterior.

The complete lower upper factorization (LU) and an incomplete lower upper factorization (ILU 4)) techniques are employed for preconditioning of the resultant linear system.

The recurrence by which it is defined allows the numbers in the sequence to be factored more easily than other numbers of the same magnitude, but, due to the rapid growth of the sequence, complete prime factorizations are known only for a few of its members.

This result generalizes the classification of doubly transitive balanced incomplete block designs with λ=1 and doubly transitive one-factorizations of complete graphs.

It is shown experimentally, that the QR factorization with the complete column pivoting, optionally followed by the LQ factorization of the R-factor, can lead to a substantial decrease of the number of outer parallel iteration steps, whereby the details depend on the condition number and on the distribution of singular values including their multiplicity.

Let F be a 2-factorization of the complete graph Kv admitting an automorphism group G acting primitively on the set of vertices.

To investigate whether the expression of the estrogen independent probe sets could capture the phenotypic complexity of ER + breast tumors we completed unsupervised clustering using non-negative matrix factorization (NMF) [ 17].

This paper intends to serve three purposes: to clarify some of the ambiguities in the original proposition, to simplify some of the computational details and, most importantly, to complete the theory of existence by matrix polynomial factorization tactics.

Secondly, PCA, a factorization method, was utilized to extract factors explaining maximum variance of data.

If you know what factors are, you can do prime factorization.

You don't have to know prime factorization to find the greatest common factor.