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Location: AENC/switchchain/powerlaw_info.tex - annotation
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Fix Constrained Configuration Model sampling
42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d 42dadc398e7d | \documentclass{article}
\begin{document}
\section{Continuous powerlaw with minimum cut-off}
Exponent $\tau > 1$.
\subsection{Also maximum cut-off}
Let $m$ be the minimum, $M$ be the maximum.\\
PDF: $$f(x) = \frac{\tau-1}{m^{-(\tau-1)} - M^{-(\tau-1)}} x^{-\tau}$$
CDF: $$F(x) = \frac{m^{-(\tau-1)} - x^{-(\tau-1)}}{m^{-(\tau-1)} - M^{-(\tau-1)}}$$
Inverse: $$F^{-1}(y) = \left( (1-y) m^{-(\tau-1)} + y M^{-(\tau-1)} \right)^{\frac{-1}{\tau-1}}$$
i.e. linear interpolate between $M^{-(\tau-1)} < m^{-(\tau-1)}$.\\
$F^{-1}(0) = m$ and $F^{-1}(1) = M$.\\
For $m=1$ and $M$ steps of interpolation:\\
$F^{-1}(1/M) = \left( 1-M^{-1} + M^{-\tau} \right)^{\frac{-1}{\tau-1}}$\\
$F^{-1}(1-1/M) = \left( M^{-1} + M^{-(\tau-1)} - M^{-\tau} \right)^{\frac{-1}{\tau-1}}$
\subsection{No maximum cut-off}
For $M=\infty$ we have:\\
PDF: $$f(x) = \frac{\tau-1}{m^{-(\tau-1)}} x^{-\tau} = \frac{\tau-1}{m} \left(\frac{x}{m}\right)^{-\tau}$$
CDF: $$F(x) = \frac{m^{-(\tau-1)} - x^{-(\tau-1)}}{m^{-(\tau-1)}} = 1 - \left(\frac{x}{m}\right)^{-(\tau-1)}$$
Inverse: $$F^{-1}(y) = \left( (1-y) m^{-(\tau-1)} \right)^{\frac{-1}{\tau-1}} = m \; \left( 1-y \right)^{\frac{-1}{\tau-1}}$$
For interpolation:\\
$F^{-1}(0) = m$\\
$F^{-1}(1-\frac{1}{n}) = m\cdot n^{\frac{1}{\tau-1}}$
\end{document}
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