# scipy.stats.bernoulli¶

scipy.stats.bernoulli

None discrete 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 pr : 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 = bernoulli(pr, loc=0, scale=1) : Frozen RV object with the same methods but holding the given shape, location, and scale fixed.

Notes

Bernoulli distribution

1 if binary experiment succeeds, 0 otherwise. Experiment succeeds with probabilty pr.

bernoulli.pmf(k,p) = 1-p if k = 0
= p if k = 1

for k = 0,1

Examples

```>>> import matplotlib.pyplot as plt
>>> numargs = bernoulli.numargs
>>> [ pr ] = Replace with reasonable value * numargs
>>> rv = bernoulli(pr)
```

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 = bernoulli.cdf(x, pr)
>>> h = plt.semilogy(np.abs(x - bernoulli.ppf(prb, pr)) + 1e-20)
```

Random number generation

```>>> R = bernoulli.rvs(pr, size=100)
```

Methods

 rvs(pr, loc=0, scale=1, size=1) Random variates. pdf(x, pr, loc=0, scale=1) Probability density function. cdf(x, pr, loc=0, scale=1) Cumulative density function. sf(x, pr, loc=0, scale=1) Survival function (1-cdf — sometimes more accurate). ppf(q, pr, loc=0, scale=1) Percent point function (inverse of cdf — percentiles). isf(q, pr, loc=0, scale=1) Inverse survival function (inverse of sf). stats(pr, loc=0, scale=1, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(pr, loc=0, scale=1) (Differential) entropy of the RV. fit(data, pr, loc=0, scale=1) Parameter estimates for generic data.

#### Previous topic

scipy.stats.binom

#### Next topic

scipy.stats.nbinom