bf.dist.mixture_general: A finite mixture of component distributions from different...

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

A finite mixture of component distributions from different families.

Description

A mixture distribution is a probability distribution constructed by selecting one of several component distributions according to specified weights, and then drawing a sample from the chosen component. It allows modelling of heterogeneous populations and multimodal data.

Usage

bf.dist.mixture_general(
  mixing_distribution,
  component_distributions,
  support = 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

mixing_distribution	A 'Categorical' distribution specifying the weights for each mixture component. The size of this distribution specifies the number of components in the mixture.
component_distributions	A list of distributions representing the components of the mixture.
support	A constraint object specifying the support of the mixture distribution. If not provided, the support will be inferred from the component distributions.
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	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	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	Integer. 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 MixtureGeneral distribution object (for model building).

- When sample=TRUE, a JAX array of samples drawn from the MixtureGeneral 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.mixture_general(
mixing_distribution = bf.dist.categorical(probs = c(0.3, 0, 7),create_obj = TRUE),
component_distributions = c(
bf.dist.normal(0,1,create_obj = TRUE),
bf.dist.normal(0,1,create_obj = TRUE),
bf.dist.normal(0,1,create_obj = TRUE)),
sample = TRUE)

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