bf.dist.two_sided_truncated_distribution: Two-Sided Truncated Distribution

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

Two-Sided Truncated Distribution

Description

A "two-sided truncated distribution" is a general concept: you take a base continuous distribution and **restrict it** to an interval (['low', 'high']), discarding all mass outside, then **renormalize** so the inner portion integrates to 1. I'll spell out the general formulas, caveats, sampling strategies, and special cases (e.g. truncated normal) to illustrate.

Usage

bf.dist.two_sided_truncated_distribution(
  base_dist,
  low = 0,
  high = 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

base_dist	The base distribution to truncate.
low	The lower bound for truncation.
high	The upper bound for truncation.
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 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	Integer representing 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. Defaults to 'FALSE'.
to_jax	Boolean. Indicates whether to return a JAX array or not.

Value

- When sample=FALSE, a BI Two-Sided Truncated distribution object (for model building).

- When sample=TRUE, a JAX array of samples drawn from the Two-Sided Truncated 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#twosidedtruncateddistribution

Examples

library(BayesForge)
m=importBF(platform='cpu')
bf.dist.two_sided_truncated_distribution(
base_dist = bf.dist.normal(0,1, create_obj = TRUE),
high = 0.5, low = 0.1, sample = TRUE)

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