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

Y ~ ShiftedGompertzCDF(concentration, rate)
pdf(y; c, r) = r * exp(-r * y - exp(-r * y) / c) * (1 + (1 - exp(-r * y)) / c)
tfb_shifted_gompertz_cdf(
  concentration,
  rate,
  validate_args = FALSE,
  name = "shifted_gompertz_cdf"
)

Arguments

concentration

Positive Float-like Tensor that is the same dtype and is broadcastable with concentration. This is c in Y = g(X) = (1 - exp(-rate * X)) * exp(-c * exp(-rate * X)).

rate

Positive Float-like Tensor that is the same dtype and is broadcastable with concentration. This is rate in Y = g(X) = (1 - exp(-rate * X)) * exp(-c * exp(-rate * X)).

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.

Value

a bijector instance.

Details

Note: Even though this is called ShiftedGompertzCDF, when applied to the Uniform distribution, this is not the same as applying a GompertzCDF with a Shift bijector (i.e. the Shifted Gompertz distribution is not the same as a Gompertz distribution with a location parameter).

Note: Because the Shifted Gompertz distribution concentrates its mass close to zero, for larger rates or larger concentrations, bijector$forward will quickly saturate to 1.

See also