slides: do the KS section

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# Kolmogorov - Smirnov test
## KS
Quantify distance between expected and observed CDF
. . .
KS statistic:
$$
D_N = \text{sup}_x |F_N(x) - F(x)|
$$
- $F(x)$ is the expected CDF
- $F_N(x)$ is the empirical CDF of $N$ sampled points
- sort points in ascending order
- number of points preceding the point normalized by $N$
## KS
$H_0$: points sampled according to $F(x)$
. . .
If $H_0$ is true:
- $\sqrt{N}D_N \xrightarrow{N \rightarrow + \infty} K$
Kolmogorov distribution with CDF:
$$
P(K \leqslant K_0) = 1 - p = \frac{\sqrt{2 \pi}}{K_0}
\sum_{j = 1}^{+ \infty} e^{-(2j - 1)^2 \pi^2 / 8 K_0^2}
$$
. . .
a $p$-value can be computed
- At 95% confidence level, $H_0$ cannot be disproved if $p > 0.05$
# Samples results
## Samples results
$N = 50000$ sampled points
. . .
Landau sample:
:::: {.columns}
::: {.column width=50%}
- $D = 0.004$
- $p = 0.379$
:::
::: {.column width=50%}
$$
\thus \text{Compatible!}
$$
:::
::::
\vspace{10pt}
. . .
Moyal sample:
:::: {.columns}
::: {.column width=50%}
- $D = 0.153$
- $p = 0.000$
:::
::: {.column width=50%}
$$
\thus \text{Not compatible!}
$$
:::
::::