Sentence examples for vector for two from inspiring English sources

In more technical terms, differential item functioning (DIF) (which replaces bias in IRT) is said to occur when a test item does not have the same relationship to a latent variable (or a multidimensional latent vector) for two or more groups of examinees.

Sections were then washed in PBST and incubated in biotinylated secondary antiserum (against rabbit IgG, 1∶1000 in PBST, Vector) for two hours, washed in PBST and incubated in ABC regents for two hours.

Given this vector for two such fragments, one can then ask, what is the probability that two fragments are in the same class ?

We then introduced the multiplex transient expression vector for five PI3K genes into Dictyostelium cells.

In Section 'Signal models with channel estimation errors', we evaluate the error displacement vector for three different channel estimation and equalization schemes related to joint/separate treatment of the mirror subcarriers.

a position vectors for two colliding particles in the lab frame and in the center-of-mass frame.

Table 2 shows the average magnitude values of motion vectors for two regions, encoded by temporal prediction and inter-view prediction.

CCA finds the basis vectors for two sets of multidimensional variables such that the correlations between the projections of the variables onto these basis vectors are mutually maximized.

Human fetal tissue was incubated with 2.105 TU of either CMV or RIP vectors for two hours at 37°C with 5% CO2 atmosphere, in 200 µl of RPMI 1640 medium supplemented with 10% heat-inactivated fetal calf serum, HEPES (10 mM), L-glutamine (2 mM), non essential amino acid (Invitrogen), penicillin (100 units/ml -streptomycin (100 µg/ml) and 10 µg/ml DEAE-dextran.

Adenovirus expression vectors for two major isoforms, C/EBPβ-liver-enriched activator protein (LAP) and - liver-enriched inhibitory protein (LIP), or small interfering RNA for C/EBPβ, were used to manipulate C/EBPβ expression in RA-FLS.

Function f can be written as The simplest kernel is the linear kernel, which is the dot product between the feature vectors for two samples: implying f is linear in the input features x.