Gumbel distribution.
Draw samples from a Gumbel distribution with specified location and scale.
For more information on the Gumbel distribution, see Notes and References
below.
Parameters : | loc : float
The location of the mode of the distribution.
scale : float
The scale parameter of the distribution.
size : tuple of ints
Output shape. If the given shape is, e.g., (m, n, k), then
m * n * k samples are drawn.
|
Returns : | out : ndarray
|
See also
scipy.stats.gumbel_l, scipy.stats.gumbel_r
- scipy.stats.genextreme
- probability density function, distribution, or cumulative density function, etc. for each of the above
weibull
Notes
The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value
Type I) distribution is one of a class of Generalized Extreme Value (GEV)
distributions used in modeling extreme value problems. The Gumbel is a
special case of the Extreme Value Type I distribution for maximums from
distributions with “exponential-like” tails.
The probability density for the Gumbel distribution is
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where
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is the mode, a location parameter, and
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is
the scale parameter.
The Gumbel (named for German mathematician Emil Julius Gumbel) was used
very early in the hydrology literature, for modeling the occurrence of
flood events. It is also used for modeling maximum wind speed and rainfall
rates. It is a “fat-tailed” distribution - the probability of an event in
the tail of the distribution is larger than if one used a Gaussian, hence
the surprisingly frequent occurrence of 100-year floods. Floods were
initially modeled as a Gaussian process, which underestimated the frequency
of extreme events.
It is one of a class of extreme value distributions, the Generalized
Extreme Value (GEV) distributions, which also includes the Weibull and
Frechet.
The function has a mean of
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and a variance of
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.
References
Gumbel, E. J., Statistics of Extremes, New York: Columbia University
Press, 1958.
Reiss, R.-D. and Thomas, M., Statistical Analysis of Extreme Values from
Insurance, Finance, Hydrology and Other Fields, Basel: Birkhauser Verlag,
2001.
Examples
Draw samples from the distribution:
>>> mu, beta = 0, 0.1 # location and scale
>>> s = np.random.gumbel(mu, beta, 1000)
Display the histogram of the samples, along with
the probability density function:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 30, normed=True)
>>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
... * np.exp( -np.exp( -(bins - mu) /beta) ),
... linewidth=2, color='r')
>>> plt.show()
(Source code, png, pdf)
Show how an extreme value distribution can arise from a Gaussian process
and compare to a Gaussian:
>>> means = []
>>> maxima = []
>>> for i in range(0,1000) :
... a = np.random.normal(mu, beta, 1000)
... means.append(a.mean())
... maxima.append(a.max())
>>> count, bins, ignored = plt.hist(maxima, 30, normed=True)
>>> beta = np.std(maxima)*np.pi/np.sqrt(6)
>>> mu = np.mean(maxima) - 0.57721*beta
>>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
... * np.exp(-np.exp(-(bins - mu)/beta)),
... linewidth=2, color='r')
>>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))
... * np.exp(-(bins - mu)**2 / (2 * beta**2)),
... linewidth=2, color='g')
>>> plt.show()
(png, pdf)