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notes: fix statistic typos

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This commit is contained in:
Michele Guerini Rocco 2020-07-06 15:14:39 +02:00
parent 747f2f4335
commit d8a00dbd10
Signed by: rnhmjoj
GPG Key ID: BFBAF4C975F76450
2 changed files with 4 additions and 4 deletions
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6
notes/sections/1.md
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@ -125,8 +125,8 @@ $$
On each iteration the function is interpolated by a parabola passing though the On each iteration the function is interpolated by a parabola passing though the
points $x_\text{min}$, $x_e$, $x_\text{max}$ and the minimum is computed as the points $x_\text{min}$, $x_e$, $x_\text{max}$ and the minimum is computed as the
vertex of the parabola. If this point is found to be inside the interval, it is vertex of the parabola. If this point is found to be inside the interval, it is
taken as a guess for the true minimum; otherwise the method falls back to a g taken as a guess for the true minimum; otherwise the method falls back to a
olden section (using the ratio $(3 - \sqrt{5})/2 \approx 0.3819660$ proven to be golden section (using the ratio $(3 - \sqrt{5})/2 \approx 0.3819660$ proven to be
optimal) of the interval. The value of the function at this new point $x'$ is optimal) of the interval. The value of the function at this new point $x'$ is
calculated. In any case, if the new point is a better estimate of the minimum, calculated. In any case, if the new point is a better estimate of the minimum,
namely if $f(x') < f(x_e)$, then the current estimate of the minimum is updated. namely if $f(x') < f(x_e)$, then the current estimate of the minimum is updated.
@ -173,7 +173,7 @@ although the result is quite imprecise.
#### Median #### Median
The median is a central tendency statistics that, unlike the mean, is not The median is a central tendency statistic that, unlike the mean, is not
very sensitive to extreme values, albeit less indicative. For this reason very sensitive to extreme values, albeit less indicative. For this reason
is well suited as test statistic in a pathological case such as the Landau is well suited as test statistic in a pathological case such as the Landau
distribution. distribution.

2
notes/sections/7.md
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@ -389,7 +389,7 @@ Aerial and lateral views of the samples. Projection line in blue and cut in red.
Using the same parameters of the training set, a number $N_t$ of test samples Using the same parameters of the training set, a number $N_t$ of test samples
was generated and the points were classified applying both methods. To avoid was generated and the points were classified applying both methods. To avoid
storing large datasets in memory, at each iteration, false positives and storing large datasets in memory, at each iteration, false positives and
negatives were recorded using a running statistics method implemented in the negatives were recorded using a running statistic method implemented in the
`gsl_rstat` library. For each sample, the numbers $N_{fn}$ and $N_{fp}$ of `gsl_rstat` library. For each sample, the numbers $N_{fn}$ and $N_{fp}$ of
false negative and false positive were obtained in this way: for every signal false negative and false positive were obtained in this way: for every signal
point $x_s$, the threshold function $f(x_s)$ was computed, then: point $x_s$, the threshold function $f(x_s)$ was computed, then: