bf.dist.asymmetric_laplace_quantile: Asymmetric Laplace Quantile Distribution

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

Asymmetric Laplace Quantile Distribution

Description

Samples from an Asymmetric Laplace Quantile distribution. This distribution is an alternative parameterization of the Asymmetric Laplace distribution, commonly used in Bayesian quantile regression. It utilizes a 'quantile' parameter to define the balance between the left- and right-hand sides of the distribution, representing the proportion of probability density that falls to the left-hand side.

Usage

bf.dist.asymmetric_laplace_quantile(
  loc = 0,
  scale = 1,
  quantile = 0.5,
  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

loc	The location parameter of the distribution.
scale	The scale parameter of the distribution.
quantile	The quantile parameter, representing the proportion of probability density to the left of the median. Must be between 0 and 1.
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 array 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. 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	The number of batch dimensions to reinterpret as event dimensions (used in model building).
create_obj	If 'TRUE', returns the raw NumPyro 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 Asymmetric Laplace Quantile distribution object (for model building).

- When sample=TRUE, a JAX array of samples drawn from the Asymmetric Laplace Quantile 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#asymmetriclaplacequantile

Examples

library(BayesForge)
m=importBF(platform='cpu')
bf.dist.asymmetric_laplace_quantile(sample = TRUE)

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