R/beta.R

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

#' @description
#'
#'Samples from a Beta distribution, defined on the interval [0, 1].
#'The Beta distribution is a versatile distribution often used to model
#'probabilities or proportions. It is parameterized by two positive shape
#'parameters, usually denoted \deqn{\alpha} and \deqn{\beta>0}, control the shape of the density (how much mass is pushed toward 0, 1, or intermediate).
#'
#'\deqn{
#'  X \sim Beta(\alpha,\beta), \\f(x)=\frac{ x^{\alpha-1}(1-x)^{\beta-1}}{B(\alpha,\beta)}, \\
#'B(\alpha,\beta)=\frac{\Gamma(\alpha)\Gamma(\beta)}{\Gamma(\alpha+\beta)}, \\F(x)=I_{x}(\alpha+\beta)
#'}
#'
#'  where \deqn{B(\alpha, \beta)} is the Beta function:
#'
#' \deqn{
#'  B(\alpha, \beta) = \int_0^1 x^{\alpha - 1} (1 - x)^{\beta - 1} , dx = \frac{\Gamma(\alpha),\Gamma (\beta)}{\Gamma(\alpha + \beta)}.
#' }
#'
#'  where \deqn{\alpha} and \deqn{\beta} are the concentration parameters, and \deqn{B(x, y)} is the Beta function.
#'
#' @title Beta Distribution
#'
#' @param concentration1 A numeric vector or array representing the first concentration parameter (shape parameter). Must be positive.
#'
#' @param concentration0 A numeric vector or array representing the second concentration parameter (shape parameter). Must be positive.
#'
#' @param 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.
#' @param event An integer representing the number of batch dimensions to reinterpret as event dimensions (used in model building).
#'
#' @param mask A logical vector or array. Optional boolean array to mask observations.
#'
#' @param create_obj A logical value. If `TRUE`, returns the raw BI distribution object instead of creating a sample
#'   site. This is essential for building complex distributions like `MixtureSameFamily`.
#'
#' @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 Beta distribution object (for model building).
#'
#'  - When \code{sample=TRUE}, a JAX array of samples drawn from the Beta distribution (for direct sampling).
#'
#'  - When \code{create_obj=TRUE}, the raw BI distribution object (for advanced use cases).
#'

#' @seealso This is a wrapper of  \url{https://num.pyro.ai/en/stable/distributions.html#beta}
#'
#' @examples
#' \donttest{
#' library(BayesForge)
#' m=importBF(platform='cpu')
#' bf.dist.beta(concentration1 = 0, concentration0 = 1, sample = TRUE)
#' }
#' @export
bf.dist.beta=function(concentration1, concentration0, 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)))
     event=as.integer(event)
     if (!.BF_env$.py$is_none(seed)){seed=as.integer(seed);}
     .BF_env$.bf_instance$dist$beta(
       concentration1 = .BF_env$jnp$array(concentration1),
       concentration0 = .BF_env$jnp$array(concentration0),
       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)
}