Sentence examples for binary variables can be from inspiring English sources

And the convex SDP problem in the form of (P3) with the pre-determined binary variables can be solved to get the optimal covariance matrix K x.

The messages for finite cardinality discrete variables (most notably for binary variables) can be easily parameterized by a number of numerically convenient representations (e.g., log-likelihood ratio, etc).

According to [23], the product of multiple binary variables can be substituted by a new variable with two constraints that ensure that if there exists, and if for all.

It is particularly useful in analysis of outbreaks of infectious diseases since a relatively large number of binary variables can be combined across a relatively small number of individuals.

Whenever binary variables can be considered as dichotomisations of a continuous latent trait (whose distribution could be transformed to a standard normal), the correlation between them can be quantified by a tetrachoric correlation coefficient (interpreted like a Pearson correlation coefficient for the latent continuous variable).

For example, the imputed values for the missing binary variable can be any real value rather than being restricted to 0 and 1.

However, if the focus is not to establish causality, or characterize risk of preterm birth, a reverse temporal model that treats X as a longitudinal dependent variable and Y as a time-invariant independent binary variable can be used to contrast exposure trajectories in the two groups.

The examples of interaction effects in SEM with binary manifest variables can be found in Woods and Grimm (2011) and Lee et al. (2016).

Assuming independence between environment and genotype in the population of controls, Begg and Zhang (3), Piegorsch et al. (2), Umbach and Weinberg (4), and Yang et al. (5) showed that efficient estimates of interaction for categorical environment and binary genotype variables can be obtained via logistic regression in a case-only analysis.

These variables can be continuous, binary or count variables and lead in turn to linear, logistic, or Poisson regression models.

Integer variables can be assigned using decimal (positive and negative), octal, hexadecimal, and binary notations.