bf.dist.gaussian_state_space: Gaussian State Space Distribution
In BayesForge: Bayesian Inference using 'numpyro' and 'XLA'

bf.dist.gaussian_state_space

R Documentation

Gaussian State Space Distribution

Description

Samples from a Gaussian state space model.

Usage

bf.dist.gaussian_state_space(
  num_steps,
  transition_matrix,
  covariance_matrix = py_none(),
  precision_matrix = py_none(),
  scale_tril = 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

`num_steps`	An integer representing the number of steps.
`transition_matrix`	A numeric vector, matrix, or array representing the state space transition matrix `A`.
`covariance_matrix`	A numeric vector, matrix, or array representing the covariance of the innovation noise `\epsilon`. Defaults to 'reticulate::py_none()'.
`precision_matrix`	A numeric vector, matrix, or array representing the precision matrix of the innovation noise `\epsilon`. Defaults to 'reticulate::py_none()'.
`scale_tril`	A numeric vector, matrix, or array representing the scale matrix of the innovation noise `\epsilon`. Defaults to 'reticulate::py_none()'.
`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, matrix, or array representing an optional boolean array to mask observations. Defaults to 'reticulate::py_none()'.
`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 specifying the shape. 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`	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. Defaults to 'FALSE'.
`to_jax`	Boolean. Indicates whether to return a JAX array or not.

Value

When 'sample=FALSE': - When 'sample=FALSE', a BI Gaussian State Space distribution object (for model building).

- When 'sample=TRUE', a JAX array of samples drawn from the Gaussian State Space 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.gaussian_state_space(
  num_steps = 1,
  transition_matrix = matrix(c(0.5), nrow = 1, byrow = TRUE),
  covariance_matrix = matrix(c(1.0), nrow = 1, byrow = TRUE),
  sample = TRUE)

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