bf.dist.multinomial: Multinomial distribution.

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

Multinomial distribution.

Description

Samples from a Multinomial distribution, which models the probability of different outcomes in a sequence of independent trials, each with a fixed number of trials and a fixed set of possible outcomes. It generalizes the binomial distribution to multiple categories.

Usage

bf.dist.multinomial(
  total_count = 1,
  probs = py_none(),
  logits = py_none(),
  total_count_max = 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

total_count	An integer or numeric vector representing the number of trials.
probs	A numeric vector representing event probabilities. Must sum to 1.
logits	A numeric vector representing event log probabilities.
total_count_max	(int, optional): An optional integer providing an upper bound on 'total_count'. This is used for performance optimization with 'lax.scan' when 'total_count' is a dynamic JAX tracer, helping to avoid recompilation.
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, 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 used 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	An integer representing the number of batch dimensions to reinterpret as event dimensions (used in model building).
create_obj	A logical value, optional. 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 Multinomial distribution object (for model building).

- When sample=TRUE, a JAX array of samples drawn from the Multinomial 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.multinomial(probs = c(0.5,0.1), sample = TRUE)

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