4

I wonder if your task is to describe the distribution of individual eigenvalues after being sorted by their absolute values (which is what Mathematica does when it returns the 4 eigenvalues). If so the following might be considered: sample = RandomVariate[NormalDistribution[0, 1.2], {10000, 4, 4}]; Tranp = Transpose /@ sample; GOEe = sample + Tranp; Eigs = ...


3

$P_v$ is a mixture of a continuous random variable and a discrete random variable (with a probability mass at 0). Mathematica's MixtureDistribution requires random variables in the mixture distribution to be all discrete or all continuous (and of the same dimension). The point is that the determination of distributions and associated summaries usually can'...


3

FindFit returns a list of replacements, not a fitted function, so Normal doesn't do anything to it. I think you meant to use NonlinearModelFit there. Nevertheless, it would not have been the right tool for the job, as you are not doing a regression, but you are fitting a distribution to your data. (I've increased the number of matrices in your sample to $10^...


2

Something like: dist = MultinormalDistribution[IdentityMatrix[2]] pts = RandomVariate[dist, 500]; Graphics[Point[pts], Axes -> True] You may chose a different variance var by e.g.: dist = MultinormalDistribution[ var IdentityMatrix[2]]


1

I don't know why you change notation and produce what seems to be a more complicated than necessary version of $g(x)$. If you just add assumptions to the integration, then you'll get an answer with your original code. I would think that defining $g(x)$ as g[x_]:= a (b/(1 + Exp[-μ Ps x/r^α + ϕ]) - 1) would be more straightforward. Here you're using the ...


Only top voted, non community-wiki answers of a minimum length are eligible