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As early as the beginning of the 20th century, it was discovered that, even in sequences of equally distributed random variables, a marginal distribution may quite naturally occur other than normal.
Ahipaşaoğlu et al. [11] also introduced a marginal distribution model to relax independently and identically distributed random variance in the logit model and applied the method of successive average to compute flexible traffic flows.
A marginal distribution, e.g., one of Eqs.
The generated synthetic slip distribution is modified to have a marginal distribution of slip values with statistics (mean and standard deviation) similar to the original slip model.
Furthermore, we assume there is a marginal distribution (rho_{X}) on X and a conditional distribution (rho yvert x)) on Y given some x.
The underlying process U may be used in all the situations where Z is a marginal distribution of a Markov chain.
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The homogeneous variance assumption allows for the distribution of effects (in a study with an arbitrary number of arms) to be expressed as a univariate marginal distribution and a series of univariate conditional distributions.
A practical framework for generating cross correlated random fields with a specified marginal distribution function, an autocorrelation function and cross correlation coefficients is presented in the paper.
This formulation leads to a complex marginal distribution for β j which is a function of | β j |.
The Copula function is a kind of special distribution function which has a uniform marginal distribution on each dimension.
Assume that {X n }n≥1is a sequence of random variables defined on a fixed probability space ( Ω, F, P ) with a common marginal distribution function F x) = P X1 ≤ x).
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