Skip to main content
`Ctrl`+`K`

# scipy.stats.loglaplace

scipy.stats.loglaplace = <scipy.stats._continuous_distns.loglaplace_gen object>

  A log-Laplace continuous random variable.

  As an instance of the `rv_continuous` class, `loglaplace` object inherits from it a collection of generic methods (see below for the full list), and completes them with details specific for this particular distribution.

  Methods

  rvs(c, loc=0, scale=1, size=1, random_state=None) | Random variates.
  pdf(x, c, loc=0, scale=1) | Probability density function.
  logpdf(x, c, loc=0, scale=1) | Log of the probability density function.
  cdf(x, c, loc=0, scale=1) | Cumulative distribution function.
  logcdf(x, c, loc=0, scale=1) | Log of the cumulative distribution function.
  sf(x, c, loc=0, scale=1) | Survival function (also defined as `1 - cdf`, but sf is sometimes more accurate).
  logsf(x, c, loc=0, scale=1) | Log of the survival function.
  ppf(q, c, loc=0, scale=1) | Percent point function (inverse of `cdf` — percentiles).
  isf(q, c, loc=0, scale=1) | Inverse survival function (inverse of `sf`).
  moment(order, c, loc=0, scale=1) | Non-central moment of the specified order.
  stats(c, loc=0, scale=1, moments=’mv’) | Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
  entropy(c, loc=0, scale=1) | (Differential) entropy of the RV.
  fit(data) | Parameter estimates for generic data. See scipy.stats.rv_continuous.fit for detailed documentation of the keyword arguments.
  expect(func, args=(c,), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) | Expected value of a function (of one argument) with respect to the distribution.
  median(c, loc=0, scale=1) | Median of the distribution.
  mean(c, loc=0, scale=1) | Mean of the distribution.
  var(c, loc=0, scale=1) | Variance of the distribution.
  std(c, loc=0, scale=1) | Standard deviation of the distribution.
  interval(confidence, c, loc=0, scale=1) | Confidence interval with equal areas around the median.

  Notes

  The probability density function for `loglaplace` is:

  \[\begin{split}f(x, c) = \begin{cases}\frac{c}{2} x^{ c-1} &\text{for } 0 < x < 1\\ \frac{c}{2} x^{-c-1} &\text{for } x \ge 1 \end{cases}\end{split}\]

  for \(c > 0\).

  `loglaplace` takes `c` as a shape parameter for \(c\).

  The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the `loc` and `scale` parameters. Specifically, `loglaplace.pdf(x, c, loc, scale)` is identically equivalent to `loglaplace.pdf(y, c) / scale` with `y = (x - loc) / scale`. Note that shifting the location of a distribution does not make it a “noncentral” distribution; noncentral generalizations of some distributions are available in separate classes.

  Suppose a random variable `X` follows the Laplace distribution with location `a` and scale `b`. Then `Y = exp(X)` follows the log-Laplace distribution with `c = 1 / b` and `scale = exp(a)`.

  References

  T.J. Kozubowski and K. Podgorski, “A log-Laplace growth rate model”, The Mathematical Scientist, vol. 28, pp. 49-60, 2003.

  Examples

      >>> import numpy as np
      >>> from scipy.stats import loglaplace
      >>> import matplotlib.pyplot as plt
      >>> fig, ax = plt.subplots(1, 1)

  Get the support:

      >>> c = 3.25
      >>> lb, ub = loglaplace.support(c)

  Calculate the first four moments:

      >>> mean, var, skew, kurt = loglaplace.stats(c, moments='mvsk')

  Display the probability density function (`pdf`):

      >>> x = np.linspace(loglaplace.ppf(0.01, c),
      ...                 loglaplace.ppf(0.99, c), 100)
      >>> ax.plot(x, loglaplace.pdf(x, c),
      ...        'r-', lw=5, alpha=0.6, label='loglaplace pdf')

  Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed.

  Freeze the distribution and display the frozen `pdf`:

      >>> rv = loglaplace(c)
      >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

  Check accuracy of `cdf` and `ppf`:

      >>> vals = loglaplace.ppf([0.001, 0.5, 0.999], c)
      >>> np.allclose([0.001, 0.5, 0.999], loglaplace.cdf(vals, c))
      True

  Generate random numbers:

      >>> r = loglaplace.rvs(c, size=1000)

  And compare the histogram:

      >>> ax.hist(r, density=True, bins='auto', histtype='stepfilled', alpha=0.2)
      >>> ax.set_xlim([x[0], x[-1]])
      >>> ax.legend(loc='best', frameon=False)
      >>> plt.show()