Sentence examples for multivariate random variable from inspiring English sources

sample of size k of a multivariate random variable (underline{X} inmathbb{R}^{m}).

Copula-based models are a general set of statistical models defined for any multivariate random variable.

Assuming each feature to be a multivariate random variable, BI is modeled as the relative entropy ΔD(p||q) between the intra-person feature distribution p(x) and the inter-person feature distribution q(x).

HapSim models a haplotype as a multivariate random variable with known marginal distributions and pairwise correlation coefficients.

In (3) and in general, the total variation of a multivariate random variable is defined as the trace of the covariance matrix, i.e.the sum of the variances of the individual variables.

Normal correlations are the means to quantify the linear relationship between two multivariate random variables.

In order to separate multivariate sources from multivariate observations, a cost function for multivariate random variables is needed.

In this regard, copulas allow to easily model the distribution of multivariate random variables by estimating only marginal pdfs and copulas.

Given historical data for continuous univariate or multivariate random variables (uncertain parameters in an optimization model), the inverse cumulative distribution function (quantile function) and the joint cumulative distribution function are estimated for the univariate and multivariate cases, respectively.

By regarding forecasting errors of different wind farms and different times as dependent multivariate random variables, high-dimensional joint probability model performs mathematical and statistical description for uncertainty characteristics through joint probability distribution function.

In previous applications we assumed that all models θ have the same causal structure, i.e. considering multivariate random variables a t and o t, we assumed that the same variables a t are intervened for all θ and the same causal model is used to predict the consequences of these interventions on the observational variables o t.