bf.dist.exponential: Samples from an Exponential distribution.

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

Samples from an Exponential distribution.

Description

The Exponential distribution is a continuous probability distribution that models the time until an event occurs in a Poisson process, where events occur continuously and independently at a constant average rate. It is often used to model the duration of events, such as the time until a machine fails or the length of a phone call.

Usage

bf.dist.exponential(
  rate = 1,
  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

rate	A numeric vector, matrix, or array representing the rate parameter, `\lambda`. Must be positive.
validate_args	A logical value indicating whether to validate the arguments. Defaults to 'TRUE'.
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 used to shape the distribution. When ‘sample=False' (model building), this is used with '.expand(shape)' 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. This is essential for building complex distributions like 'MixtureSameFamily'.
to_jax	Boolean. Indicates whether to return a JAX array or not.

Value

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

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

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

See Also

This is a wrapper of https://num.pyro.ai/en/stable/distributions.html#exponential

Examples

library(BayesForge)
m=importBF(platform='cpu')
bf.dist.exponential(rate = c(0.1,1,2),sample = TRUE)

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