bf.dist.gaussian_copula: Gaussian Copula Distribution

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

Gaussian Copula Distribution

Description

A distribution that links the 'batch_shape[:-1]' of a marginal distribution with a multivariate Gaussian copula, odelling the correlation between the axes. A copula is a multivariate distribution over the uniform distribution on [0, 1]. The Gaussian copula links the marginal distributions through a multivariate normal distribution.

Usage

bf.dist.gaussian_copula(
  marginal_dist,
  correlation_matrix = py_none(),
  correlation_cholesky = 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

marginal_dist	Distribution: Distribution whose last batch axis is to be coupled.
correlation_matrix	array_like, optional: Correlation matrix of the coupling multivariate normal distribution. Defaults to 'reticulate::py_none()'.
correlation_cholesky	array_like, optional: Correlation Cholesky factor of the coupling multivariate normal distribution. Defaults to 'reticulate::py_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	jnp.ndarray, bool, optional: Optional boolean array 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	numeric vector: A multi-purpose argument for shaping. 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	int: The number of batch dimensions to reinterpret as event dimensions (used in model building).
create_obj	bool, optional: If 'TRUE', returns the raw BI distribution object instead of creating a sample site. This is essential for building complex distributions like 'MixtureSameFamily'. Defaults to 'FALSE'.
to_jax	Boolean. Indicates whether to return a JAX array or not.

Value

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

- When sample=TRUE, a JAX array of samples drawn from the Gaussian Copula 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.gaussian_copula(
  marginal_dist = bf.dist.gamma(concentration = 1 ,  create_obj = TRUE) ,
  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.