mode#
- Uniform.mode(*, method=None)[source]#
- Mode (most likely value) - 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 \(\chi\) that maximizes the probability density function \(f(x)\): \[\text{mode} = \arg\max_{x \in \chi} f(x)\]- 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
 - Not all method options are available for all distributions. If the selected method is not available, a - NotImplementedErrorwill be raised.
 
- Returns:
- outarray
- 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 debateable 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, - modewill- 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.- References [1]- Mode (statistics), Wikipedia, https://en.wikipedia.org/wiki/Mode_(statistics) - Examples - Instantiate a distribution with the desired parameters: - >>> from scipy import stats >>> X = stats.Normal(mu=1., sigma=2.) - Evaluate the mode: - >>> X.mode() 1.0 - If the mode is not uniquely defined, - modenonetheless returns a single value.- >>> X = stats.Uniform(a=0., b=1.) >>> X.mode() 0.5 - 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() 1.0