Exponential distribution.

Inherits From: Gamma

The Exponential distribution is parameterized by an event rate parameter.

Mathematical Details

The probability density function (pdf) is,

pdf(x; lambda, x > 0) = exp(-lambda x) / Z
Z = 1 / lambda

where rate = lambda and Z is the normalizaing constant.

The Exponential distribution is a special case of the Gamma distribution, i.e.,

Exponential(rate) = Gamma(concentration=1., rate)

The Exponential distribution uses a rate parameter, or "inverse scale", which can be intuited as,

X ~ Exponential(rate=1)
Y = X / rate

rate Floating point tensor, equivalent to 1 / mean. Must contain only positive values.
validate_args Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value "NaN" to indicate the result is undefined. When False, an exception is raised if one or more of the statistic's batch members are undefined.
name Python str name prefixed to Ops created by this class.

allow_nan_stats Python bool describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)**2] is also undefined.