bf.dist.categorical: Sample from a Categorical distribution.

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

Sample from a Categorical distribution.

Description

The Categorical distribution, also known as the multinomial distribution, describes the probability of different outcomes from a finite set of possibilities. It is commonly used to model discrete choices or classifications.

P(k) = \frac{e^{\log(p_k)}}{\sum_{j=1}^{K} e^{\log(p_j)}}

where

p_k

is the probability of outcome

k

, and the sum is over all possible outcomes.

Usage

bf.dist.categorical(
  probs = py_none(),
  logits = 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

probs	A numeric vector of probabilities for each category. Must sum to 1.
logits	A numeric vector of Log-odds of each category.
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 vector 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	Logical; 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 Categorical distribution object (for model building).

- When sample=TRUE, a JAX array of samples drawn from the Categorical 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.categorical(probs = c(0.5,0.5), sample = TRUE, shape = c(3))

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