Sentence examples for by the multivariate probability from inspiring English sources

The degree of coincidence statistically depends upon the spatial dependence of precipitation and can be measured by the multivariate probability distribution.

Many approaches to estimate the applicability domain of a QSAR model either define the geometric subset of the descriptor space that is spanned by the training set, or infer the multivariate probability distribution of the respective descriptor vectors [20 24].

Building environmental contours of extreme sea-states thus requires the multivariate probability distribution of such variables.

In this paper, by extending the concept of "area metric" and the "u-pooling method" developed for validating a single response, we propose new model validation metrics for validating correlated multiple responses using the multivariate probability integral transformation (PIT).

A common practical approach of constructing the multivariate probability distribution of Y is to transform Y into standard normal data X and construct the multivariate standard normal distribution for X.

Under this hypothesis the CPDF of group i (i = M or N) is given by the multivariate normal probability density (6) p (x | i ) = 1 (2 π ) d / 2 | Σ i | − 1 / 2 exp ⁡ { − 1 2 (x − μ i ) ⊤ Σ i − 1 (x − μ i ) } where μ i and Σ i are the mean and the covariance matrix of class i, d the number of predictor variables used for discrimination and superscript T indicates matrix transposition.

This paper presents a novel approach to statistical modeling of instantaneous solar irradiance by using a multivariate probability distribution a copula to describe the dependency between beam and diffuse indices.

The construction and simulation of multivariate probability distributions and their event probabilities is one of the current challenges in probability theory and statistics.

102 The result is a simple score that provides probability estimates that correspond to the score generated by the multivariate regression model.

The probability of identifying these 21 genes by chance if there are no real differences between the classes was 0.056 as determined by the multivariate permutation test.

Under the assumption that these two hypotheses are represented in the relevant decision space by multivariate Gaussian probability density functions of equal variance, the raw hit rates and the false-alarm rates can be linearized by application of the inverse, cumulative Gaussian transform; their difference after linearization is the discrimination index, or d', of signal-detection theory.