pmf#
- Uniform.pmf(x, /, *, method=None)[source]#
- Probability mass function - The probability mass function (“PMF”), denoted \(f(x)\), is the probability that the random variable \(X\) will assume the value \(x\). \[f(x) = P(X = x)\]- pmfaccepts x for \(x\).- Parameters:
- xarray_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 - NotImplementedErrorwill be raised.
 
- Returns:
- outarray
- 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, - pmfreturns- 0at all real arguments.- References [1]- Probability mass function, Wikipedia, https://en.wikipedia.org/wiki/Probability_mass_function - 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(0.24609375)