bf.dist.weibull: Samples from a Weibull distribution.

In BayesForge: Bayesian Inference using 'numpyro' and 'XLA'

Samples from a Weibull distribution.

Description

The Weibull distribution is widely used for modeling **lifetime or reliability data**. Its shape parameter (k) controls the hazard function: * (k < 1): decreasing hazard (infant mortality) * (k = 1): constant hazard ??? reduces to **Exponential distribution** * (k > 1): increasing hazard (aging/failure over time)

Usage

bf.dist.weibull(
  scale,
  concentration,
  validate_args = py_none(),
  name = "x",
  obs = py_none(),
  mask = py_none(),
  sample = FALSE,
  seed = py_none(),
  shape = c(),
  event = 0,
  create_obj = FALSE,
  to_jax = TRUE
)

Arguments

scale	A numeric vector, matrix, or array representing the scale parameter of the Weibull distribution. Must be positive.
concentration	A numeric vector, matrix, or array representing the shape parameter of the Weibull distribution. Must be positive.
validate_args	Logical: Whether to validate parameter values. Defaults to 'reticulate::py_none()'.
name	A character string representing the name of the random variable within a model. This is used to uniquely identify the variable. Defaults to 'x'.
obs	A numeric vector or array of observed values. If provided, the random variable is conditioned on these values. If 'NULL', the variable is treated as a latent (unobserved) variable. Defaults to 'NULL'.
mask	An optional boolean vector to mask observations.
sample	A logical value that controls the function's behavior. If 'TRUE', the function will directly draw samples from the distribution. If 'FALSE', it will create a random variable within a model. Defaults to 'FALSE'.
seed	An integer used to set the random seed for reproducibility when 'sample = TRUE'. This argument has no effect when 'sample = FALSE', as randomness is handled by the model's inference engine. Defaults to 0.
shape	A numeric vector. This is used with ‘.expand(shape)' when 'sample=False' (model building) to set the distribution’s batch shape. When 'sample=True' (direct sampling), this is used as 'sample_shape' to draw a raw JAX array of the given shape.
event	An integer representing the number of batch dimensions to reinterpret as event dimensions (used in model building).
create_obj	A logical value. If 'TRUE', returns the raw BI distribution object instead of creating a sample site.
to_jax	Boolean. Indicates whether to return a JAX array or not.

Value

- When sample=FALSE, a BI Weibull distribution object (for model building).

- When sample=TRUE, a JAX array of samples drawn from the Weibull distribution (for direct sampling).

- When create_obj=TRUE, the raw BI distribution object (for advanced use cases).

See Also

https://num.pyro.ai/en/stable/distributions.html#weibull

Examples

library(BayesForge)
m=importBF(platform='cpu')
bf.dist.weibull(scale = c(10, 10), concentration = c(1,1), sample = TRUE)

BayesForge documentation built on June 9, 2026, 1:09 a.m.