bf.dist.negative_binomial2: Samples from a Negative Binomial distribution.

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

Samples from a Negative Binomial distribution.

Description

The NB2 parameterisation of the negative-binomial distribution is a count distribution used for modelling over-dispersed count data (variance > mean). It is defined such that the variance grows **quadratically** in the mean:

Var(Y) = \mu + \alpha,\mu^2,

where

\mu = E[Y]) and (\alpha>0)

is the dispersion (heterogeneity) parameter. Because of this quadratic variance growth, it is called the NB2 family.

P(k) = \frac{\Gamma(k + \alpha)}{\Gamma(k + 1) \Gamma(\alpha)} \left(\frac{\beta}{\alpha + \beta}\right)^k \left(1 - \frac{\beta}{\alpha + \beta}\right)^k

Usage

bf.dist.negative_binomial2(
  total_count,
  probs,
  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

total_count	(int): The number of trials *n*.
probs	A numeric vector, matrix, or array representing the probability of success for each Bernoulli trial. Must be between 0 and 1.
logits	A numeric vector, matrix, or array representing the log-odds of success for each trial.
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 logical 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. Used with ‘.expand(shape)' when 'sample=False' (model building) to set the distribution’s batch shape. When 'sample=True' (direct sampling), 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 Negative Binomial distribution object (for model building).

- When sample=TRUE, a JAX array of samples drawn from the Negative Binomial distribution (for direct sampling).

- When create_obj=TRUE, the raw BI distribution object (for advanced use cases).

See Also

https://num.pyro.ai/en/stable/distributions.html#negativebinomial2

Examples

library(BayesForge)
m=importBF(platform='cpu')
bf.dist.negative_binomial2(total_count = 100, probs = 0.5, sample = TRUE)

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