# scipy.stats.mielke¶

scipy.stats.mielke()

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 : string, 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’) mielke.rvs(k,s,loc=0,scale=1,size=1) : random variates mielke.pdf(x,k,s,loc=0,scale=1) : probability density function mielke.cdf(x,k,s,loc=0,scale=1) : cumulative density function mielke.sf(x,k,s,loc=0,scale=1) : survival function (1-cdf — sometimes more accurate) mielke.ppf(q,k,s,loc=0,scale=1) : percent point function (inverse of cdf — percentiles) mielke.isf(q,k,s,loc=0,scale=1) : inverse survival function (inverse of sf) mielke.stats(k,s,loc=0,scale=1,moments=’mv’) : mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’) mielke.entropy(k,s,loc=0,scale=1) : (differential) entropy of the RV. mielke.fit(data,k,s,loc=0,scale=1) : Parameter estimates for mielke data 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

Examples

```>>> import matplotlib.pyplot as plt
>>> numargs = mielke.numargs
>>> [ k,s ] = [0.9,]*numargs
>>> rv = mielke(k,s)
```

Display frozen pdf

```>>> x = np.linspace(0,np.minimum(rv.dist.b,3))
>>> h=plt.plot(x,rv.pdf(x))
```

Check accuracy of cdf and ppf

```>>> prb = mielke.cdf(x,k,s)
>>> h=plt.semilogy(np.abs(x-mielke.ppf(prb,c))+1e-20)
```

Random number generation

```>>> R = mielke.rvs(k,s,size=100)
```

Mielke’s Beta-Kappa distribution

mielke.pdf(x,k,s) = k*x**(k-1) / (1+x**s)**(1+k/s) for x > 0.

#### Previous topic

scipy.stats.maxwell

#### Next topic

scipy.stats.nakagami