A Planck discrete exponential random variable.
Discrete random variables are defined from a standard form and may require some shape parameters to complete its specification. Any optional keyword parameters can be passed to the methods of the RV object as given below:
Parameters: | x : array_like
q : array_like
lambda_ : array_like
loc : array_like, optional
size : int or tuple of ints, optional
moments : str, optional
Alternatively, the object may be called (as a function) to fix the shape and location parameters returning a “frozen” discrete RV object: rv = planck(lambda_, loc=0)
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Notes
The probability mass function for planck is:
planck.pmf(k) = (1-exp(-lambda_))*exp(-lambda_*k)
for k*lambda_ >= 0.
planck takes lambda_ as shape parameter.
Examples
>>> from scipy.stats import planck
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)
Calculate a few first moments:
>>> lambda_ = 0.51
>>> mean, var, skew, kurt = planck.stats(lambda_, moments='mvsk')
Display the probability mass function (pmf):
>>> x = np.arange(planck.ppf(0.01, lambda_),
... planck.ppf(0.99, lambda_))
>>> ax.plot(x, planck.pmf(x, lambda_), 'bo', ms=8, label='planck pmf')
>>> ax.vlines(x, 0, planck.pmf(x, lambda_), colors='b', lw=5, alpha=0.5)
Alternatively, freeze the distribution and display the frozen pmf:
>>> rv = planck(lambda_)
>>> ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-', lw=1,
... label='frozen pmf')
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()
Check accuracy of cdf and ppf:
>>> prob = planck.cdf(x, lambda_)
>>> np.allclose(x, planck.ppf(prob, lambda_))
True
Generate random numbers:
>>> r = planck.rvs(lambda_, size=1000)
Methods
rvs(lambda_, loc=0, size=1) | Random variates. |
pmf(x, lambda_, loc=0) | Probability mass function. |
logpmf(x, lambda_, loc=0) | Log of the probability mass function. |
cdf(x, lambda_, loc=0) | Cumulative density function. |
logcdf(x, lambda_, loc=0) | Log of the cumulative density function. |
sf(x, lambda_, loc=0) | Survival function (1-cdf — sometimes more accurate). |
logsf(x, lambda_, loc=0) | Log of the survival function. |
ppf(q, lambda_, loc=0) | Percent point function (inverse of cdf — percentiles). |
isf(q, lambda_, loc=0) | Inverse survival function (inverse of sf). |
stats(lambda_, loc=0, moments=’mv’) | Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). |
entropy(lambda_, loc=0) | (Differential) entropy of the RV. |
expect(func, lambda_, loc=0, lb=None, ub=None, conditional=False) | Expected value of a function (of one argument) with respect to the distribution. |
median(lambda_, loc=0) | Median of the distribution. |
mean(lambda_, loc=0) | Mean of the distribution. |
var(lambda_, loc=0) | Variance of the distribution. |
std(lambda_, loc=0) | Standard deviation of the distribution. |
interval(alpha, lambda_, loc=0) | Endpoints of the range that contains alpha percent of the distribution |