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bundles / scipy 1.17.1 / scipy / stats / _probability_distribution / _ProbabilityDistribution / mode

function

scipy.stats._probability_distribution:_ProbabilityDistribution.mode

source: /scipy/stats/_probability_distribution.py :441

Signature

def   mode ( self ,  * ,  method )

Summary

Mode (most likely value)

Extended Summary

Informally, the mode is a value that a random variable has the highest probability (density) of assuming. That is, the mode is the element of the support that maximizes the probability density (or mass, for discrete random variables) function :

Parameters

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

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

  • 'formula': use a formula for the median

  • 'optimization': numerically maximize the PDF/PMF

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 mode

Notes

For some distributions

  • the mode is not unique (e.g. the uniform distribution);

  • the PDF has one or more singularities, and it is debatable whether a singularity is considered to be in the domain and called the mode (e.g. the gamma distribution with shape parameter less than 1); and/or

  • the probability density function may have one or more local maxima that are not a global maximum (e.g. mixture distributions).

In such cases, mode will

  • return a single value,

  • consider the mode to occur at a singularity, and/or

  • return a local maximum which may or may not be a global maximum.

If a formula for the mode is not specifically implemented for the chosen distribution, SciPy will attempt to compute the mode numerically, which may not meet the user's preferred definition of a mode. In such cases, the user is encouraged to subclass the distribution and override mode.

Examples

Instantiate a distribution with the desired parameters: 
from scipy import stats
X = stats.Normal(mu=1., sigma=2.)
✓
Evaluate the mode: 
X.mode()
✗
If the mode is not uniquely defined, ``mode`` nonetheless returns a single value. 
X = stats.Uniform(a=0., b=1.)
✓
X.mode()
✗
If this choice does not satisfy your requirements, subclass the distribution and override ``mode``: 
class BetterUniform(stats.Uniform):
    def mode(self):
        return self.b
X = BetterUniform(a=0., b=1.)
✓
X.mode()
✗

See also

mean
median
pdf

Aliases

  • scipy.stats._distribution_infrastructure._ProbabilityDistribution.mode