User:Lothar.brendel: Difference between revisions

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(→‎Scratch Pad: Florians eCDF)
m (→‎Florian's eCDF: same ~p_n)
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$$
$$
c(n_i)-c(n_{i-1})\stackrel{*}{=}c(n_i)-c(n_i-1)=c(n_i)-c(n_i-\epsilon)=p_{n_i}~.
c(n_i)-c(n_{i-1})\stackrel{*}{=}c(n_i)-c(n_i-1)=c(n_i)-c(n_i-\epsilon)=p_{n_i}~.
$$ Florian's modified eCDF replaces the step-function c by a piecewise linear function c~ with c~(ni)=c(ni), from which one obtains fictious empirical probabilities p~n=c~(n)c~(n1) for ni1<nni. Due to the "natural" Δn=1 of the Steigungsdreieck, those p~n coincide with the slope
$$ Florian's modified eCDF replaces the step-function c by a piecewise linear function c~ with c~(ni)=c(ni), from which one obtains fictious empirical probabilities p~n=c~(n)c~(n1) for ni1<nni. Due to the "natural" Δn=1 of the Steigungsdreieck, those \(\tilde p_n\) (being all the same because of the linearity) coincide with the slope
$$
$$
s_{n_i}=\frac{\tilde c(n_i)-\tilde c(n_{i-1})}{n_i-n_{i-1}} = \frac{c(n_i)-c(n_{i-1})}{n_i-n_{i-1}}~,
s_{n_i}=\frac{\tilde c(n_i)-\tilde c(n_{i-1})}{n_i-n_{i-1}} = \frac{c(n_i)-c(n_{i-1})}{n_i-n_{i-1}}~,
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\sum_{n=n_{i-1}+1}^{n_i} \tilde p_n = (n_{i}-n_{i-1})\tilde p_n = (n_{i}-n_{i-1})s_{n_i} = c(n_i)-c(n_{i-1}) = p_{n_i}~.
\sum_{n=n_{i-1}+1}^{n_i} \tilde p_n = (n_{i}-n_{i-1})\tilde p_n = (n_{i}-n_{i-1})s_{n_i} = c(n_i)-c(n_{i-1}) = p_{n_i}~.
$$
$$
Hence, the effect of using c~ is replacing the "bar" pni and the zeros to its left by smaller "bars" with the same height p~n while preserving the probabilities, kp~k=1. In other words, it's a left-biased coarse graining.
Hence, the effect of using c~ is replacing the "bar" pni and the zeros to its left by smaller "bars" of equal height p~n while preserving probability, which of course implies kp~k=1. In other words, the scheme is a left-biased coarse graining.





Revision as of 11:57, 10 July 2024

Lothar Brendel

"Admin" of this Wiki

Scratch Pad

2D flow in polar coordinates (ρ,ϕ)

  • vorticity: ω=(×v)ez=ρvϕ+vϕϕvρρ
  • its gradient: ω=eρρω+eϕρϕω

Energy equation

E=ρe+ρ2u2E=E+ρϕtE=tE+ϕtρ=((E+P)u)=((E+P)u+ϕρu)=((E+P)u)ϕ(ρu)uρϕtE=((E+P)u)+uf

Florian's eCDF

Let pn be the empirical probabilities and let pn=0 for ni1<n<ni. Then, being a step-function, the eCDF fulfills c(ni)c(ni1)=c(ni)c(ni1)=c(ni)c(niϵ)=pni . Florian's modified eCDF replaces the step-function c by a piecewise linear function c~ with c~(ni)=c(ni), from which one obtains fictious empirical probabilities p~n=c~(n)c~(n1) for ni1<nni. Due to the "natural" Δn=1 of the Steigungsdreieck, those p~n (being all the same because of the linearity) coincide with the slope sni=c~(ni)c~(ni1)nini1=c(ni)c(ni1)nini1 , which implies n=ni1+1nip~n=(nini1)p~n=(nini1)sni=c(ni)c(ni1)=pni . Hence, the effect of using c~ is replacing the "bar" pni and the zeros to its left by smaller "bars" of equal height p~n while preserving probability, which of course implies kp~k=1. In other words, the scheme is a left-biased coarse graining.


*: even though in general ni1ni1

Hamiltons principle