bf.dist.logistic: Samples from a Logistic distribution.

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

Samples from a Logistic distribution.

Description

The Logistic distribution is a continuous probability distribution defined by two parameters: location and scale. It is often used to model growth processes and is closely related to the normal distribution. Its CDF is the logistic (sigmoid) function, which makes it appealing in modeling probabilities, logistic regression, and various growth models. It resembles the normal distribution in shape (bell-shaped, symmetric) but has **heavier tails** (i.e. more probability in the extremes) and simpler closed-form expressions for the CDF.

Usage

bf.dist.logistic(
  loc = 0,
  scale = 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

loc	Numeric vector or single number. The location parameter, specifying the median of the distribution. Defaults to 0.0.
scale	Numeric vector or single number. The scale parameter, which determines the spread of the distribution. Must be positive. Defaults to 1.0.
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	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	Numeric vector. A multi-purpose argument 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	Integer. 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. This is essential for building complex distributions like 'MixtureSameFamily'.
to_jax	Boolean. Indicates whether to return a JAX array or not.

Value

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

- When sample=TRUE, a JAX array of samples drawn from the Logistic 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.logistic(sample = TRUE)

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