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