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tfb_weibull_cdf

Compute Y = g(X) = 1 - exp((-X / scale) ** concentration), X >= 0.


Description

This bijector maps inputs from [0, inf] to [0, 1]. The inverse of the bijector applied to a uniform random variable X ~ U(0, 1) gives back a random variable with the Weibull distribution:

Y ~ Weibull(scale, concentration)
pdf(y; scale, concentration, y >= 0) =
  (concentration / scale) * (y / scale)**(concentration - 1) *
    exp(-(y / scale)**concentration)

Usage

tfb_weibull_cdf(
  scale = 1,
  concentration = 1,
  validate_args = FALSE,
  name = "weibull_cdf"
)

Arguments

scale

Positive Float-type Tensor that is the same dtype and is broadcastable with concentration. This is l in Y = g(X) = 1 - exp((-x / l) ** k).

concentration

Positive Float-type Tensor that is the same dtype and is broadcastable with scale. This is k in Y = g(X) = 1 - exp((-x / l) ** k).

validate_args

Logical, default FALSE. Whether to validate input with asserts. If validate_args is FALSE, and the inputs are invalid, correct behavior is not guaranteed.

name

name prefixed to Ops created by this class.

Details

Likwewise, the forward of this bijector is the Weibull distribution CDF.

Value

a bijector instance.

See Also


tfprobability

Interface to 'TensorFlow Probability'

v0.11.0.0
Apache License (>= 2.0)
Authors
Sigrid Keydana [aut, cre], Daniel Falbel [ctb], Kevin Kuo [ctb] (<https://orcid.org/0000-0001-7803-7901>), RStudio [cph]
Initial release

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