bf.dist.beta_binomial: BetaBinomial

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

BetaBinomial

Description

Samples from a BetaBinomial distribution, a compound distribution where the probability of success in a binomial experiment is drawn from a Beta distribution. This models situations where the underlying probability of success is not fixed but varies according to a prior belief represented by the Beta distribution. It is often used to model over-dispersion relative to the binomial distribution.

Usage

bf.dist.beta_binomial(
  concentration1,
  concentration0,
  total_count = 1,
  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

concentration1	A numeric vector, matrix, or array representing the first concentration parameter (alpha) of the Beta distribution.
concentration0	A numeric vector, matrix, or array representing the second concentration parameter (beta) of the Beta distribution.
total_count	A numeric vector, matrix, or array representing the number of Bernoulli trials in the Binomial part of the distribution.
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. 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	An integer representing the number of batch dimensions to reinterpret as event dimensions (used in model building).
create_obj	A logical value. 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 Beta-Binomial distribution object (for model building).

- When sample=TRUE, a JAX array of samples drawn from the Beta-Binomial 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#betabinomial

Examples

library(BayesForge)
m=importBF(platform='cpu')
bf.dist.beta_binomial(0,1,sample = TRUE)

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