`scipy.stats._probability_distribution:_ProbabilityDistribution.entropy`

source: /scipy/stats/_probability_distribution.py :1901

Signature

def entropy ( self , * , method )

Summary

Differential entropy

Extended Summary

In terms of probability density function $f (x)$ and support $χ$ , the differential entropy (or simply "entropy") of a continuous random variable $X$ is:

h (X) = - \int_{χ} f (x) lo g f (x) d x

The definition for a discrete random variable is analogous, with the PMF replacing the PDF and a sum over the support replacing the integral.

Parameters

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

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

'formula': use a formula for the entropy itself
'logexp': evaluate the log-entropy and exponentiate
'quadrature': numerically integrate (or, in the discrete case, sum) the entropy integrand (summand)

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 entropy of the random variable.

Notes

This function calculates the entropy using the natural logarithm; i.e. the logarithm with base $e$ . Consequently, the value is expressed in (dimensionless) "units" of nats. To convert the entropy to different units (i.e. corresponding with a different base), divide the result by the natural logarithm of the desired base.

Examples

Instantiate a distribution with the desired parameters:

from scipy import stats
X = stats.Uniform(a=-1., b=1.)

✓

Evaluate the entropy:

X.entropy()

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Aliases

scipy.stats._distribution_infrastructure._ProbabilityDistribution.entropy