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In probability theory and statistics, the logistic distribution is a continuous probability distribution. Its cumulative distribution function is the logistic function, which appears in logistic regression and feedforward neural networks. It resembles the normal distribution in shape but has heavier tails (higher kurtosis). The logistic distribution is a…
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logistic distribution function cumulative displaystyle normal mu quantile regression probability mathrm beta density scale distributions standard mean derivative terms sim
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Logistic distribution | CDF | 1 1 + e − ( x − μ ) / s = 1 + tanh x − μ 2 s 2 {\displaystyle {\frac {1}{1+e^{-(x-\mu )/s}}}={\frac {1+\tanh {\frac {x-\mu }{2s}}}{2}}} | 1.00 | infobox |
| Logistic distribution | CF | e i t μ π s t sinh ( π s t ) {\displaystyle e^{it\mu }{\frac {\pi st}{\sinh(\pi st)}}} | 1.00 | infobox |
| Logistic distribution | Entropy | ln s + 2 {\displaystyle \ln s+2} | 1.00 | infobox |
| Logistic distribution | Excess kurtosis | 6 / 5 {\displaystyle 6/5} | 1.00 | infobox |
| Logistic distribution | Mean | μ {\displaystyle \mu } | 1.00 | infobox |
| Logistic distribution | Median | μ {\displaystyle \mu } | 1.00 | infobox |
| Logistic distribution | MGF | e μ t B ( 1 − s t , 1 + s t ) {\displaystyle e^{\mu t}\mathrm {B} (1-st,1+st)} for t ∈ ( − 1 / s , 1 / s ) {\displaystyle t\in (-1/s,1/s)} and B {\displaystyle \mathrm {B} } is… | 1.00 | infobox |
| Logistic distribution | Mode | μ {\displaystyle \mu } | 1.00 | infobox |
| Logistic distribution | Parameters | μ , {\displaystyle \mu ,} location (real) s > 0 , {\displaystyle s>0,} scale (real) | 1.00 | infobox |
| Logistic distribution | e − ( x − μ ) / s s ( 1 + e − ( x − μ ) / s ) 2 {\displaystyle {\frac {e^{-(x-\mu )/s}}{s\left(1+e^{-(x-\mu )/s}\right)^{2}}}} | 1.00 | infobox | |
| Logistic distribution | Quantile | μ + s log ( p 1 − p ) {\displaystyle \mu +s\log \left({\frac {p}{1-p}}\right)} | 1.00 | infobox |
| Logistic distribution | Skewness | 0 {\displaystyle 0} | 1.00 | infobox |
| Logistic distribution | Support | x ∈ ( − ∞ , ∞ ) {\displaystyle x\in (-\infty ,\infty )} | 1.00 | infobox |
| Logistic distribution | Variance | s 2 π 2 3 {\displaystyle {\frac {s^{2}\pi ^{2}}{3}}} | 1.00 | infobox |
| Logistic distribution | is a | continuous probability distribution | 0.90 | text |
| Logistic distribution | is a | special case of the Tukey lambda distribution | 0.90 | text |
| Logistic distribution | is a | generalization of the logit function | 0.90 | text |
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