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

In tfprobability: Interface to 'TensorFlow Probability'

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

For usage examples see tfb_forward(), tfb_inverse(), tfb_inverse_log_det_jacobian().

Other bijectors: tfb_absolute_value(), tfb_affine_linear_operator(), tfb_affine_scalar(), tfb_affine(), tfb_ascending(), tfb_batch_normalization(), tfb_blockwise(), tfb_chain(), tfb_cholesky_outer_product(), tfb_cholesky_to_inv_cholesky(), tfb_correlation_cholesky(), tfb_cumsum(), tfb_discrete_cosine_transform(), tfb_expm1(), tfb_exp(), tfb_ffjord(), tfb_fill_scale_tri_l(), tfb_fill_triangular(), tfb_glow(), tfb_gompertz_cdf(), tfb_gumbel_cdf(), tfb_gumbel(), tfb_identity(), tfb_inline(), tfb_invert(), tfb_iterated_sigmoid_centered(), tfb_kumaraswamy_cdf(), tfb_kumaraswamy(), tfb_lambert_w_tail(), tfb_masked_autoregressive_default_template(), tfb_masked_autoregressive_flow(), tfb_masked_dense(), tfb_matrix_inverse_tri_l(), tfb_matvec_lu(), tfb_normal_cdf(), tfb_ordered(), tfb_pad(), tfb_permute(), tfb_power_transform(), tfb_rational_quadratic_spline(), tfb_rayleigh_cdf(), tfb_real_nvp_default_template(), tfb_real_nvp(), tfb_reciprocal(), tfb_reshape(), tfb_scale_matvec_diag(), tfb_scale_matvec_linear_operator(), tfb_scale_matvec_lu(), tfb_scale_matvec_tri_l(), tfb_scale_tri_l(), tfb_scale(), tfb_shifted_gompertz_cdf(), tfb_shift(), tfb_sigmoid(), tfb_sinh_arcsinh(), tfb_sinh(), tfb_softmax_centered(), tfb_softplus(), tfb_softsign(), tfb_split(), tfb_square(), tfb_tanh(), tfb_transform_diagonal(), tfb_transpose(), tfb_weibull()

tfprobability documentation built on Sept. 1, 2022, 5:07 p.m.