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I think there are two issues: (1) How to get done what you want to get done?, and (2) Should you be doing what you want? To answer the second question one would need to know the purpose for your graphic. For the first part another option is to... |
I believe you that the "maxima show rolloff effects." But what does that mean? I don't speak that language. Might there be a need to include that "effect" in the modeling? |
I don't know what you mean by "slow". The following works after about 60 seconds: dist = TransformedDistribution[Abs[a - b] + Abs[u - v], {a \[Distributed] UniformDistribution[], b \[Distributed] UniformDistribution[], ... |
While it's annoying to get error messages that aren't easily interpreted, if your data was from biological data that I typically deal with, I'd say you have no peaks as the minor fluctuations that appear look more like random noise. Are the... |
I think there are potentially 3 things to change: 1. `P` should be a number between 0 and 1. 2. `Fisher2Math` should be `Fisher2Math[k_, p_] := {k, (1 - p)/p}` 3. `Grille` should not include 0 and 1 and should be `Grille = Range[0.05, 0.95,... |
It would be help (in fact, essential) that you produce the code you used for at least `NonlinearModelFit`. Generally when fitting sine waves to data with `NonlinearModelFit`, there needs to be a reasonable guess at the parameter representing the... |
Setting values for $b$ and $h$ results in warnings that the integration doesn't converge on $(0,\infty)$. Here's an example: Integrate[Sinh[x]/((Cosh[x] + Cosh[b])*(h^2 - x^2)) /. {h -> 1, b -> 1}, {x, 0, \[Infinity]}] > Integrate::idiv:... |
The result for any positive integer `n` is the sum of two terms: (1) an `Exp[x y]` term multiplied by a polynomial in `x` and `y` and (2) a polynomial in `x` and `y'. The first term is Exp[x y] LaguerreL[n, (x - y)^2]/n! but I haven't... |
Gianluca: It's when each of those terms are multiplied by an unassigned symbolic value where the automatic simplification does not occur. |
My speculation about the appearance of complex numbers is that *Mathematica* uses the pdf of `1/(1/s+1/t)` which contains logs: dist = TransformedDistribution[1/(1/s + 1/t), {t \[Distributed] UniformDistribution[{-1, 1}], s... |