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T copula: this is derived from the Student's t multivariate distribution and is defined as where |ρ| < 1, θ > 0.
The "control" networks are from the null model, where the expression data are generated from an independent multivariate distribution and none of the genes are connected.
LDA presumes that the classification variables follow a normal multivariate distribution and the covariance matrices for the observations of each class are equal (homoscedasticity).
Here, we assume that all original data from different data sets follow some unknown joint multivariate distribution and all data tuples are samples from this distribution.
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Three methods of creating multivariate copulas were applied and the results were compared using (1) Kendall's measure; (2) empirical multivariate distributions; and (3) simulations.
The posterior for the sparse linear model with Laplace prior does not fall into any standard multivariate distribution family, and it is not known how to do computations with it analytically.
Assuming familiarity with standard probability and multivariate distribution theory, we will provide a discussion of the mathematical and theoretical foundation for Bayesian inferential procedures.
where is the normal multivariate distribution with mean vector and covariance matrix, is the number of clusters, and is the prior probability that the observation has been generated by cluster.
Tail dependence is an important feature in modeling accuracy and conventional wind speed scenario generation methods such as autoregressive integrated moving average (ARIMA) and multivariate distribution functions are not capable of modeling tail dependence.
In some cases, this multivariate distribution may be multimodal and does not necessarily follow a simple closed-form distribution.
The goodness-of-fit evaluation of multivariate distribution needs huge computation and is time-consuming, especially for high dimensional cases.
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