bf.dist.truncated_cauchy: Truncated Cauchy Distribution

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

Truncated Cauchy Distribution

Description

The Cauchy distribution, also known as the Lorentz distribution, is a continuous probability distribution that appears frequently in various areas of mathematics and physics. It is characterized by its heavy tails, which extend to infinity. The truncated version limits the support of the Cauchy distribution to a specified interval.

Usage

bf.dist.truncated_cauchy(
  loc = 0,
  scale = 1,
  low = py_none(),
  high = py_none(),
  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	Location parameter of the Cauchy distribution.
scale	Scale parameter of the Cauchy distribution.
low	(float, jnp.ndarray, optional): The lower truncation point. If 'None', the distribution is only truncated on the right. Defaults to 'None'.
high	(float, jnp.ndarray, optional): The upper truncation point. If 'None', the distribution is only truncated on the left. Defaults to 'None'. validate_args (bool, optional): Whether to enable validation of distribution parameters. Defaults to 'None'.
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	Logical; 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 Truncated Cauchy distribution object (for model building).

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

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

Examples

library(BayesForge)
m=importBF(platform='cpu')
bf.dist.truncated_cauchy(loc = 0, scale = 2, low = 0, high = 1.5, sample = TRUE)

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