Latin Hypercube Sampling is a nearly random sampling method, but the method of construction means that the sample sets derived will more thoroughly represent the

sample space. The samples which had values of MET > 2, CLO < 0.7 and CLO > 1.3, and [V.sub.a] > 0.2 m/s were removed in order for the samples to better represent office conditions where ASHRAE Standard 55 was applicable.

The disadvantage of the neighbor algorithm is that the time complexity of the algorithm is high and the distance between the samples to be classified in the classification process and each sample in the known

sample space should be calculated in turn.

(a) Interpolated

sample space; (b) the predicted value.

The basic idea of PCA is to decompose multivariable

sample space into lower dimensional principal component subspaces composed of principal components variables and a residual subspace according to the historical data of process variables.

(ii) Let ([OMEGA], A, P) be a probability space; that is, [OMEGA] alone is called the

sample space, A is a [sigma]-algebra on [OMEGA], and P is a probability measure on ([OMEGA], A).

As the nearest neighbor of the selected

sample space increases, its computational complexity is also increased, and the computational complexity is O([N.sup.2]) (equivalent to N * N matrix calculation).

If the

sample space was too sparse, namely, the main states were random movements in free space, it was difficult to achieve a stable training effect.

The large

sample space of the data helped the team control several variables related to demography such as age, gender, marital status, political affiliation, and education which were deemed crucial.

Define and delimit the

sample space or universe (U) where the phenomena or events that will be studied occur.

Hence there exists no

sample space and as such bivariate or multivariate models to measure the longevities under competing risk are not possible.