Sentence examples for multivariate distribution where from inspiring English sources

The test is executed for example instances of the multivariate distribution, where D = 1, 2, 4, 10 and asking the algorithms to generate 10 to 1,000 samples.

Thus, in order to generate samples from correlated multivariate distributions, it is necessary to transform the random variable space to obtain an uncorrelated multivariate distribution, where independent RVs can be used.

First, for each set of residues of interest, the set is considered as a 3 N-dimensional multivariate distribution, where N is the number of non-hydrogen atoms in the set, and the multivariate normal approximation of the differential entropy H is calculated from the non-hydrogen atomic fluctuation covariance matrix using Carma and in-house software.

A copula is defined as a multivariate probability distribution where the marginal probability distribution of each variable is uniform and is used to describe the dependency between random variables [22, 29 33].

To do this, we generate B bootstrap samples from the multivariate normal distribution, where is the covariance matrix of.

The model for the gene matrix follows [ 5], where Y follows a multivariate normal distribution: where y gi represents the expression observation for gth gene in the ith sample, μ is the mean vector and Σ is the covariance matrix.

It is shown [ 2- 4] that correlation r is only a limited description of the dependence between random variables except for the multivariate normal distribution where the correlation fully describes the dependence structure.

The expression data matrix Y observed here has G genes and N samples, and the model follows [ 24] and [ 25], where Y follows a multivariate normal distribution: where y ji represents the expression observation for jth gene in the ith sample, μ is the mean vector and Σ is the covariance matrix.

or The regression parameters for an individual patient are selected from a multivariate normal distribution where is the vector of expected parameter values for k th MCMC parameter set to the covariate effects of the i th individual and Σ is the covariance matrix.

For the second example, the first group is drawn from the multivariate log-normal distribution, where each of the 5 dimensions consists of independent, univariate log-normal draws with location parameter 0 and scale parameter 1.

Using a standard result (e.g., Srivastava, 2002, Theorem 2.5.1), follows the -dimensional multivariate normal (MVN) distribution:, where For convenience we have labeled the mean and variance parameters of using the generic symbols, not because they are all equal, but because they are equally irrelevant to subsequent development.