`scipy.stats._probability_distribution:_ProbabilityDistribution.pmf`

source: /scipy/stats/_probability_distribution.py:993

Signature

def pmf(self, x, /, method = None)

Summary

Probability mass function

Extended Summary

The probability mass function ("PMF"), denoted $f (x)$ , is the probability that the random variable $X$ will assume the value $x$ .

f (x) = P (X = x)

pmf accepts x for $x$ .

Parameters

x : array_like

The argument of the PMF.

method : {None, 'formula', 'logexp'}

The strategy used to evaluate the PMF. By default (None), the infrastructure chooses between the following options, listed in order of precedence.

'formula': use a formula for the PMF itself
'logexp': evaluate the log-PMF and exponentiate

Not all method options are available for all distributions. If the selected method is not available, a NotImplementedError will be raised.

Returns

out : array: The PMF evaluated at the argument x.

Notes

Suppose a discrete probability distribution has support over the integers $l, l + 1, ..., r - 1, r$ . By definition of the support, the PMF evaluates to its minimum value of $0$ for non-integral $x$ and for $x$ outside the support; i.e. for $x < l$ or $x > r$ .

For continuous distributions, pmf returns 0 at all real arguments.

Examples

Instantiate a distribution with the desired parameters:

from scipy import stats
X = stats.Binomial(n=10, p=0.5)

✓

Evaluate the PMF at the desired argument:

X.pmf(5)

✗

np.float64(0.24609375000000003)

Aliases

scipy.stats._distribution_infrastructure._ProbabilityDistribution.pmf