bf.dist.cauchy: Cauchy Distribution

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

Cauchy Distribution

Description

Samples from a Cauchy distribution.

The Cauchy distribution, also known as the Lorentz distribution, is a continuous probability distribution that arises frequently in various fields, including physics and statistics. It is characterized by its heavy tails, which extend indefinitely. This means it has a higher probability of extreme values compared to the normal distribution.

Usage

bf.dist.cauchy(
  loc = 0,
  scale = 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

loc	A numeric vector or scalar representing the location parameter. Defaults to 0.0.
scale	A numeric vector or scalar representing the scale parameter. Must be positive. Defaults to 1.0.
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	A logical vector, optional, to mask observations. Defaults to 'reticulate::py_none()'.
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 specifying the shape of 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). Defaults to 'reticulate::py_none()'.
create_obj	A logical value. If 'TRUE', returns the raw BI distribution object instead of creating a sample site. Defaults to 'FALSE'.
to_jax	Boolean. Indicates whether to return a JAX array or not.

Value

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

- When sample=TRUE, a JAX array of samples drawn from the Cauchy 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#cauchy

Examples

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

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