R/poisson.R

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

#' @title Poisson Distribution
#'
#' @description
#' The **Poisson distribution** models the probability of observing a given number of events $k$ occurring in a fixed interval
#' of time or space when these events happen independently and at a constant average rate \deqn{\lambda > 0 }.
#' It is widely used for modeling **count data**, such as the number of emails received per hour or mutations in a DNA strand per unit length.

#' Formally,
#' \deqn{
#'   K \sim \text{Poisson}(\lambda)
#' }
#' where \deqn{ \lambda } is both the **mean** and **variance** of the distribution.
#'
#' @param rate A numeric vector representing the average number of events.
#' @param  is_sparse (bool, optional): Indicates whether the `rate` parameter is sparse. If `True`, a specialized sparse sampling implementation is used, which can be more efficient for models with many zero-rate components (e.g., zero-inflated models). Defaults to `False`.
#' @param 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.
#' @param event An integer representing the number of batch dimensions to reinterpret as event dimensions (used in model building).
#' @param mask A logical vector to mask observations.
#' @param create_obj A logical value. If `TRUE`, returns the raw BI distribution object instead of creating a sample site.
#' @param validate_args Logical: Whether to validate parameter values.  Defaults to `reticulate::py_none()`.
#' @param 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`.
#' @param 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.
#' @param 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`.
#' @param 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'.
#' @param to_jax Boolean. Indicates whether to return a JAX array or not.
#'
#' @return
#'  - When \code{sample=FALSE}, a BI Poisson distribution object (for model building).
#'
#'  - When \code{sample=TRUE}, a JAX array of samples drawn from the Poisson distribution (for direct sampling).
#'
#'  - When \code{create_obj=TRUE}, the raw BI distribution object (for advanced use cases).
#'
#' @examples
#' \donttest{
#' library(BayesForge)
#' m=importBF(platform='cpu')
#' bf.dist.poisson(rate = c(0.2, 0.5, 0.8), sample = TRUE)
#' }
#' @export
bf.dist.poisson=function(rate, is_sparse=FALSE, 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) {
     shape=do.call(tuple, as.list(as.integer(shape)))
     if (!.BF_env$.py$is_none(seed)){seed=as.integer(seed);}
     .BF_env$.bf_instance$dist$poisson(
       rate = .BF_env$jnp$array(rate),
       is_sparse= is_sparse,  validate_args= validate_args,  name= name,  obs= obs,  mask= mask,  sample= sample,  seed= seed,  shape= shape,  event= event,  create_obj= create_obj,   to_jax = to_jax)
}