# scipy.stats.bernoulli¶

scipy.stats.bernoulli()

A bernoulli discrete 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:

Methods: bernoulli.rvs(pr,loc=0,size=1) : random variates bernoulli.pmf(x,pr,loc=0) : probability mass function bernoulli.cdf(x,pr,loc=0) : cumulative density function bernoulli.sf(x,pr,loc=0) : survival function (1-cdf — sometimes more accurate) bernoulli.ppf(q,pr,loc=0) : percent point function (inverse of cdf — percentiles) bernoulli.isf(q,pr,loc=0) : inverse survival function (inverse of sf) bernoulli.stats(pr,loc=0,moments=’mv’) : mean(‘m’,axis=0), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’) bernoulli.entropy(pr,loc=0) : entropy of the RV Alternatively, the object may be called (as a function) to fix : the shape and location parameters returning a : “frozen” discrete RV object: : myrv = bernoulli(pr,loc=0) : frozen RV object with the same methods but holding the given shape and location fixed. You can construct an aribtrary discrete rv where P{X=xk} = pk : by passing to the rv_discrete initialization method (through the values= : keyword) a tuple of sequences (xk,pk) which describes only those values of : X (xk) that occur with nonzero probability (pk). :

Examples

```>>> import matplotlib.pyplot as plt
>>> numargs = bernoulli.numargs
>>> [ pr ] = ['Replace with resonable value',]*numargs
```

Display frozen pmf:

```>>> rv = bernoulli(pr)
>>> x = np.arange(0,np.min(rv.dist.b,3)+1)
>>> h = plt.plot(x,rv.pmf(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)
```

Custom made discrete distribution:

```>>> vals = [arange(7),(0.1,0.2,0.3,0.1,0.1,0.1,0.1)]
>>> custm = rv_discrete(name='custm',values=vals)
>>> h = plt.plot(vals[0],custm.pmf(vals[0]))
```

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

#### Previous topic

scipy.stats.binom

#### Next topic

scipy.stats.nbinom