saplings were analysed using a multivariate method.
4) Statistical classification using a multivariate Gaussian classifier.
Independent risk factors were analysed using a multivariate logistic regression.
Extraction conditions were optimized using a multivariate approach.
While the example presented (Table 4 and Figure 3) used only a single CNP as an illustration, the LRTS can be calculated for multiple SNPs analyzed simultaneously through specification of a multivariate pdf.
It is shown in (3) that a multivariate PDF is constructed by multiplying a copula density function and a set of marginal PDFs in which the copula density function can be selected independent of the margins.
To combine multiple markers, we used a multivariate normal distribution to model the probability density function (pdf) for each class.
An adaptive Kinect is selected using a bivariate Gaussian PDF.
SIMB sequences were produced by randomly selecting BBI values using a PDF and imparting memory with an autocorrelation-based function.
We then demonstrate the functionality using a full text PDF article as an example.
