Usually, a noise in the GMANOVA model is assumed to be distributed some Gaussian distribution.
For example, in studies on the time to the occurrence of tumors in human populations or in laboratory animals, the time of occurrence of tumors is generally assumed to be distributed as a Weibull distribution.
This random variable is assumed to be distributed as a normal distribution with mean μ and variance σ.
Because the clinical performances of drugs are proportions, performance samples θ ikt are assumed to be distributed with a Beta distribution [ 21].
Therefore, the loss rate variable is assumed to be distributed according to a gamma distribution, which is by far the most popular distribution for describing rate variability [ 24, 33].
And instead of determining the P value of y from a χ2 distribution, y was assumed to be distributed according to F 1,L) and the P values were calculated from that distribution.
For frame detection in time domain, these two cases are considered equal, because the time domain multicarrier signal is assumed to be distributed according to a normal distribution, yielding similar characteristics as the noise [13].
The LRT statistic was assumed to be distributed with a chi-square distribution with one df.
The contaminant flux in this case was assumed to be distributed according to a bivariate normal distribution.
The log-spectrum vector y Tr of the noisy training speech was assumed to be distributed as a mixture of Gaussian distributions with mean vectors and covariance matrices obtained through a vector quantization process using the noisy training data.
R0 is assumed to be diagonal, i.e., p(R0)is the product of two independent scaled inverted Chi-square distributions (for GL and CD, because was set to 1 to ensure identification), and p(G0) and p(H0) are assumed to be distributed a priori as inverted Wishart distributions.
