# scipy.stats.mielke¶

scipy.stats.mielke = <scipy.stats._continuous_distns.mielke_gen object at 0x4e05710>[source]

A Mielke’s Beta-Kappa continuous random variable.

Continuous 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 quantiles q : array_like lower or upper tail probability k, s : array_like shape parameters loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) size : int or tuple of ints, optional shape of random variates (default computed from input arguments ) moments : str, optional composed of letters [‘mvsk’] specifying which moments to compute where ‘m’ = mean, ‘v’ = variance, ‘s’ = (Fisher’s) skew and ‘k’ = (Fisher’s) kurtosis. (default=’mv’) Alternatively, the object may be called (as a function) to fix the shape, : location, and scale parameters returning a “frozen” continuous RV object: : rv = mielke(k, s, loc=0, scale=1) : Frozen RV object with the same methods but holding the given shape, location, and scale fixed.

Notes

The probability density function for mielke is:

```mielke.pdf(x, k, s) = k * x**(k-1) / (1+x**s)**(1+k/s)
```

for x > 0.

Examples

```>>> from scipy.stats import mielke
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)
```

Calculate a few first moments:

```>>> k, s = 10.4, 3.6
>>> mean, var, skew, kurt = mielke.stats(k, s, moments='mvsk')
```

Display the probability density function (pdf):

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

Alternatively, freeze the distribution and display the frozen pdf:

```>>> rv = mielke(k, s)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
```

Check accuracy of cdf and ppf:

```>>> vals = mielke.ppf([0.001, 0.5, 0.999], k, s)
>>> np.allclose([0.001, 0.5, 0.999], mielke.cdf(vals, k, s))
True
```

Generate random numbers:

```>>> r = mielke.rvs(k, s, size=1000)
```

And compare the histogram:

```>>> ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()
```

Methods

 rvs(k, s, loc=0, scale=1, size=1) Random variates. pdf(x, k, s, loc=0, scale=1) Probability density function. logpdf(x, k, s, loc=0, scale=1) Log of the probability density function. cdf(x, k, s, loc=0, scale=1) Cumulative density function. logcdf(x, k, s, loc=0, scale=1) Log of the cumulative density function. sf(x, k, s, loc=0, scale=1) Survival function (1-cdf — sometimes more accurate). logsf(x, k, s, loc=0, scale=1) Log of the survival function. ppf(q, k, s, loc=0, scale=1) Percent point function (inverse of cdf — percentiles). isf(q, k, s, loc=0, scale=1) Inverse survival function (inverse of sf). moment(n, k, s, loc=0, scale=1) Non-central moment of order n stats(k, s, loc=0, scale=1, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(k, s, loc=0, scale=1) (Differential) entropy of the RV. fit(data, k, s, loc=0, scale=1) Parameter estimates for generic data. expect(func, k, s, 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(k, s, loc=0, scale=1) Median of the distribution. mean(k, s, loc=0, scale=1) Mean of the distribution. var(k, s, loc=0, scale=1) Variance of the distribution. std(k, s, loc=0, scale=1) Standard deviation of the distribution. interval(alpha, k, s, loc=0, scale=1) Endpoints of the range that contains alpha percent of the distribution

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