bf.dist.mixture: A marginalized finite mixture of component distributions.
In BayesForge: Bayesian Inference using 'numpyro' and 'XLA'

bf.dist.mixture

R Documentation

A marginalized finite mixture of component distributions.

Description

This distribution represents a mixture of component distributions, where the mixing weights are determined by a Categorical distribution. The resulting distribution can be either a MixtureGeneral (when component distributions are a list) or a MixtureSameFamily (when component distributions are a single distribution).

Usage

bf.dist.mixture(
  mixing_distribution,
  component_distributions,
  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.
`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 used 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 of the distribution.
`event`	An integer representing the number of batch dimensions to reinterpret as event dimensions.
`create_obj`	A logical value. If 'TRUE', returns the raw BI distribution object.
`to_jax`	Boolean. Indicates whether to return a JAX array or not.

Value

When sample=FALSE: A BI Mixture distribution object (for model building). When sample=TRUE: A JAX array of samples drawn from the Mixture 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(
  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.