Draw samples from an exponential distribution.
Its probability density function is
for x > 0 and 0 elsewhere. \(\beta\) is the scale parameter,
which is the inverse of the rate parameter \(\lambda = 1/\beta\).
The rate parameter is an alternative, widely used parameterization
of the exponential distribution 3.
x > 0
The exponential distribution is a continuous analogue of the
geometric distribution. It describes many common situations, such as
the size of raindrops measured over many rainstorms 1, or the time
between page requests to Wikipedia 2.
scale (float or array_like of floats) – The scale parameter, \(\beta = 1/\lambda\).
size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g., (m, n, k), then
m * n * k samples are drawn. If size is None (default),
a single value is returned if scale is a scalar. Otherwise,
np.array(scale).size samples are drawn.
(m, n, k)
m * n * k
chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension
gpu (bool, optional) – Allocate the tensor on GPU if True, False as default
dtype (data-type, optional) – Data-type of the returned tensor.
out – Drawn samples from the parameterized exponential distribution.
Tensor or scalar
Peyton Z. Peebles Jr., “Probability, Random Variables and
Random Signal Principles”, 4th ed, 2001, p. 57.
Wikipedia, “Poisson process”,
Wikipedia, “Exponential distribution”,