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- In probability theory, an exponentially modified Gaussian distribution (EMG, also known as exGaussian distribution) describes the sum of independent normal and exponential random variables. An exGaussian random variable Z may be expressed as Z = X + Y, where X and Y are independent, X is Gaussian with mean μ and variance σ2, and Y is exponential of rate λ. It has a characteristic positive skew from the exponential component. It may also be regarded as a weighted function of a shifted exponential with the weight being a function of the normal distribution. (en)
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- 16599 (xsd:nonNegativeInteger)
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- where (en)
- is the CDF of a Gaussian distribution (en)
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- λ > 0 (en)
- μ ∈ R (en)
- σ2 > 0 (en)
- — mean of Gaussian component (en)
- — rate of exponential component (en)
- — variance of Gaussian component (en)
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- In probability theory, an exponentially modified Gaussian distribution (EMG, also known as exGaussian distribution) describes the sum of independent normal and exponential random variables. An exGaussian random variable Z may be expressed as Z = X + Y, where X and Y are independent, X is Gaussian with mean μ and variance σ2, and Y is exponential of rate λ. It has a characteristic positive skew from the exponential component. It may also be regarded as a weighted function of a shifted exponential with the weight being a function of the normal distribution. (en)
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- Exponentially modified Gaussian distribution (en)
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