tfp.edward2.LinearGaussianStateSpaceModel

tfp.edward2.LinearGaussianStateSpaceModel(
    *args,
    **kwargs
)

Create a random variable for LinearGaussianStateSpaceModel.

See LinearGaussianStateSpaceModel for more details.

Returns:

RandomVariable.

Original Docstring for Distribution

Initialize a `LinearGaussianStateSpaceModel.

Args:

  • num_timesteps: Integer Tensor total number of timesteps.
  • transition_matrix: A transition operator, represented by a Tensor or LinearOperator of shape [latent_size, latent_size], or by a callable taking as argument a scalar integer Tensor t and returning a Tensor or LinearOperator representing the transition operator from latent state at time t to time t + 1.
  • transition_noise: An instance of tfd.MultivariateNormalLinearOperator with event shape [latent_size], representing the mean and covariance of the transition noise model, or a callable taking as argument a scalar integer Tensor t and returning such a distribution representing the noise in the transition from time t to time t + 1.
  • observation_matrix: An observation operator, represented by a Tensor or LinearOperator of shape [observation_size, latent_size], or by a callable taking as argument a scalar integer Tensor t and returning a timestep-specific Tensor or LinearOperator.
  • observation_noise: An instance of tfd.MultivariateNormalLinearOperator with event shape [observation_size], representing the mean and covariance of the observation noise model, or a callable taking as argument a scalar integer Tensor t and returning a timestep-specific noise model.
  • initial_state_prior: An instance of MultivariateNormalLinearOperator representing the prior distribution on latent states; must have event shape [latent_size].
  • initial_step: optional int specifying the time of the first modeled timestep. This is added as an offset when passing timesteps t to (optional) callables specifying timestep-specific transition and observation models.
  • validate_args: Python bool, default False. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
  • allow_nan_stats: Python bool, default True. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
  • name: The name to give Ops created by the initializer.