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In probability theory and statistics, the exponential distribution or negative exponential distribution is the probability distribution of the distance between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate; the distance parameter could be any meaningful mono-dimensional…
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Explore the main themes, entities and connections around Exponential distribution. Start with the topic map, then use the sections below for research and deeper semantic analysis.
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| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Exponential distribution | CDF | 1 − e − λ x {\displaystyle 1-e^{-\lambda x}} | 1.00 | infobox |
| Exponential distribution | CF | λ λ − i t {\displaystyle {\frac {\lambda }{\lambda -it}}} | 1.00 | infobox |
| Exponential distribution | Entropy | 1 − ln λ {\displaystyle 1-\ln \lambda } | 1.00 | infobox |
| Exponential distribution | Excess kurtosis | 6 {\displaystyle 6} | 1.00 | infobox |
| Exponential distribution | Fisher information | 1 λ 2 {\displaystyle {\frac {1}{\lambda ^{2}}}} | 1.00 | infobox |
| Exponential distribution | Kullback–Leibler divergence | ln λ 0 λ + λ λ 0 − 1 {\displaystyle \ln {\frac {\lambda _{0}}{\lambda }}+{\frac {\lambda }{\lambda _{0}}}-1} | 1.00 | infobox |
| Exponential distribution | Mean | 1 λ {\displaystyle {\frac {1}{\lambda }}} | 1.00 | infobox |
| Exponential distribution | Median | ln 2 λ {\displaystyle {\frac {\ln 2}{\lambda }}} | 1.00 | infobox |
| Exponential distribution | MGF | λ λ − t , for t < λ {\displaystyle {\frac {\lambda }{\lambda -t}},{\text{ for }}t<\lambda } | 1.00 | infobox |
| Exponential distribution | Mode | 0 {\displaystyle 0} | 1.00 | infobox |
| Exponential distribution | Parameters | λ > 0 , {\displaystyle \lambda >0,} rate, or inverse scale | 1.00 | infobox |
| Exponential distribution | λ e − λ x {\displaystyle \lambda e^{-\lambda x}} | 1.00 | infobox | |
| Exponential distribution | Quantile | − ln ( 1 − p ) λ {\displaystyle -{\frac {\ln(1-p)}{\lambda }}} | 1.00 | infobox |
| Exponential distribution | Skewness | 2 {\displaystyle 2} | 1.00 | infobox |
| Exponential distribution | Support | x ∈ [ 0 , ∞ ) {\displaystyle x\in [0,\infty )} | 1.00 | infobox |
| Exponential distribution | Variance | 1 λ 2 {\displaystyle {\frac {1}{\lambda ^{2}}}} | 1.00 | infobox |
| Exponential distribution | is a | probability distribution of the distance between events in a Poisson point process | 0.90 | text |
| Exponential distribution | is a | special case of type 3 Pearson distribution.For any λ | 0.90 | text |
| Exponential distribution | is a | limit of the κ-exponential distribution in the κ | 0.90 | text |
| Exponential distribution | is a | gamma distribution | 0.90 | text |
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