Table of contents
The density function of the Exponential distribution:
f(x; \lambda) = \lambda \exp(-\lambda x) \times \mathbf{1}[ x \geq 0]
Methods for scalar input, as well as for list input, are listed below.
.. autofunction:: pystats.dexp(x: float, rate: float = 1.0, log: bool = False) -> float :noindex:
.. autofunction:: pystats.dexp(x: List[float], rate: float = 1.0, log: bool = False) -> List[float] :noindex:
The cumulative distribution function (CDF) of the Exponential distribution:
\int_0^x f(z; \lambda) dz = 1 - \exp(-\lambda x \times \mathbf{1}[ x \geq 0])
Methods for scalar input, as well as for list input, are listed below.
.. autofunction:: pystats.pexp(p: float, rate: float = 1.0, log: bool = False) -> float :noindex:
.. autofunction:: pystats.pexp(p: List[float], rate: float = 1.0, log: bool = False) -> List[float] :noindex:
The quantile function of the Exponential distribution:
q(p; \lambda) = - \ln (1 - p) / \lambda
Methods for scalar input, as well as for list input, are listed below.
.. autofunction:: pystats.qexp(q: float, rate: float = 1.0) -> float :noindex:
.. autofunction:: pystats.qexp(q: List[float], rate: float = 1.0) -> List[float] :noindex:
Random sampling for the Cauchy distribution is achieved via the inverse probability integral transform.
.. autofunction:: pystats.rexp(rate: float = 1.0) -> float :noindex:
.. autofunction:: pystats.rexp(n: int, rate: float = 1.0) -> List[float] :noindex: