bf.dist.gaussian_copula_beta: Gaussian Copula Beta distribution.

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

Gaussian Copula Beta distribution.

Description

This distribution combines a Gaussian copula with a Beta distribution. The Gaussian copula models the dependence structure between random variables, while the Beta distribution defines the marginal distributions of each variable.

Usage

bf.dist.gaussian_copula_beta(
  concentration1,
  concentration0,
  correlation_matrix = py_none(),
  correlation_cholesky = py_none(),
  validate_args = FALSE,
  name = "x",
  obs = py_none(),
  mask = py_none(),
  sample = FALSE,
  seed = py_none(),
  shape = c(),
  event = 0,
  create_obj = FALSE,
  to_jax = TRUE
)

Arguments

concentration1	A numeric vector or matrix representing the first shape parameter of the Beta distribution.
concentration0	A numeric vector or matrix representing the second shape parameter of the Beta distribution.
correlation_matrix	array_like, optional: Correlation matrix of the coupling multivariate normal distribution. Defaults to 'reticulate::py_none()'.
correlation_cholesky	A numeric vector, matrix, or array representing the Cholesky decomposition of the correlation matrix.
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 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. This is used as 'sample_shape' to draw a raw JAX array of the given shape when 'sample=True'.
event	Integer indicating 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.
to_jax	Boolean. Indicates whether to return a JAX array or not.

Value

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

- When sample=TRUE, a JAX array of samples drawn from the Gaussian Copula Beta 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#gaussiancopulabetadistribution

Examples

library(BayesForge)
m=importBF(platform='cpu')
bf.dist.gaussian_copula_beta(
  concentration1 = c(2.0, 3.0),
  concentration0 = c(5.0, 3.0),
  correlation_matrix = matrix(c(1.0, 0.7, 0.7, 1.0), nrow = 2, byrow = TRUE),
  sample = TRUE)
  

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