bf.dist.wishart: Wishart distribution for covariance matrices.

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

Wishart distribution for covariance matrices.

Description

The Wishart distribution is a multivariate distribution used to model positive definite matrices, often representing covariance matrices. It's commonly used in Bayesian statistics and machine learning, particularly in models involving covariance estimation.

Usage

bf.dist.wishart(
  concentration,
  scale_matrix = py_none(),
  rate_matrix = py_none(),
  scale_tril = 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

concentration	A positive concentration parameter analogous to the concentration of a Gamma distribution. The concentration must be larger than the dimensionality of the scale matrix.
scale_matrix	A scale matrix analogous to the inverse rate of a Gamma distribution.
rate_matrix	A rate matrix anaologous to the rate of a Gamma distribution.
scale_tril	Cholesky decomposition of the 'scale_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	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 specifying the shape. 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 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 Wishart distribution object (for model building).

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

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

See Also

https://num.pyro.ai/en/stable/distributions.html#wishart

Examples

library(BayesForge)
m=importBF(platform='cpu')
bf.dist.wishart(concentration = 5, scale_matrix = matrix(c(1,0,0,1), nrow = 2), sample = TRUE)

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