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Logistic distribution

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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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}}}
CF
e i t μ π s t sinh ⁡ ( π s t ) {\displaystyle e^{it\mu }{\frac {\pi st}{\sinh(\pi st)}}}
Entropy
ln ⁡ s + 2 {\displaystyle \ln s+2}
Excess kurtosis
6 / 5 {\displaystyle 6/5}
Mean
μ {\displaystyle \mu }
Median
μ {\displaystyle \mu }

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Logistic distribution

Nodes56
Edges55
Triples82
Avg. degree1.96
Density0.035714
Components1

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Logistic distribution

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related to References · 24
Logistic distribution → American Statistical Association, Balakrishnan, Continuous Univariate Distributions, Forecasts, Future, Handbook, ISBN, John, Johnson, JSTOR, Kotz, Marcel Dekker, Modis, New York, Past, Predictions, Robert, Schuster, Simon, Society's Telltale Signature Reveals
related to Physics · 8
Logistic distribution → Bernoulli, Dirac, Fermi, However, In, The, The PDF, Those
related to Related distributions · 8
Logistic distribution → Champernowne, Exponential, Gumbel, If, Logistic, LogLogistic, The, X-Y
related to Logistic regression · 5
Logistic distribution → However, Indeed, One, Specifically, This
related to Hydrology · 4
Logistic distribution → As, In, October, The
is a · 3
Logistic distribution → continuous probability distribution, generalization of the logit function, special case of the Tukey lambda distribution
related to Chess ratings · 3
Logistic distribution → Elo, Federation, The United States Chess
related to Quantile function · 3
Logistic distribution → Its, The, They
has application · 2
Logistic distribution → S-shaped, The
related to Alternative parameterization · 2
Logistic distribution → An, The

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logistic distribution function cumulative displaystyle normal mu quantile regression probability mathrm beta density scale distributions standard mean derivative terms sim

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SubjectPredicateObjectConfidenceSrc
Logistic distributionCDF1 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}}}1.00infobox
Logistic distributionCFe i t μ π s t sinh ⁡ ( π s t ) {\displaystyle e^{it\mu }{\frac {\pi st}{\sinh(\pi st)}}}1.00infobox
Logistic distributionEntropyln ⁡ s + 2 {\displaystyle \ln s+2}1.00infobox
Logistic distributionExcess kurtosis6 / 5 {\displaystyle 6/5}1.00infobox
Logistic distributionMeanμ {\displaystyle \mu }1.00infobox
Logistic distributionMedianμ {\displaystyle \mu }1.00infobox
Logistic distributionMGFe 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…1.00infobox
Logistic distributionModeμ {\displaystyle \mu }1.00infobox
Logistic distributionParametersμ , {\displaystyle \mu ,} location (real) s > 0 , {\displaystyle s>0,} scale (real)1.00infobox
Logistic distributionPDFe − ( 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}}}}1.00infobox
Logistic distributionQuantileμ + s log ⁡ ( p 1 − p ) {\displaystyle \mu +s\log \left({\frac {p}{1-p}}\right)}1.00infobox
Logistic distributionSkewness0 {\displaystyle 0}1.00infobox
Logistic distributionSupportx ∈ ( − ∞ , ∞ ) {\displaystyle x\in (-\infty ,\infty )}1.00infobox
Logistic distributionVariances 2 π 2 3 {\displaystyle {\frac {s^{2}\pi ^{2}}{3}}}1.00infobox
Logistic distributionis acontinuous probability distribution0.90text
Logistic distributionis aspecial case of the Tukey lambda distribution0.90text
Logistic distributionis ageneralization of the logit function0.90text

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