Build a variational posterior that factors over model parameters.

The surrogate posterior consists of independent Normal distributions for each parameter with trainable loc and scale, transformed using the parameter's bijector to the appropriate support space for that parameter.

sts_build_factored_surrogate_posterior(
  model,
  batch_shape = list(),
  seed = NULL,
  name = NULL
)

Arguments

model	An instance of StructuralTimeSeries representing a time-series model. This represents a joint distribution over time-series and their parameters with batch shape [b1, ..., bN].#'
batch_shape	Batch shape (list, or integer) of initial states to optimize in parallel. Default value: list(). (i.e., just run a single optimization).
seed	integer to seed the random number generator.
name	string prefixed to ops created by this function. Default value: NULL (i.e., 'build_factored_surrogate_posterior').

Value

variational_posterior tfd_joint_distribution_named defining a trainable surrogate posterior over model parameters. Samples from this distribution are named lists with character parameter names as keys.

See also

Other sts-functions: sts_build_factored_variational_loss(), sts_decompose_by_component(), sts_decompose_forecast_by_component(), sts_fit_with_hmc(), sts_forecast(), sts_one_step_predictive(), sts_sample_uniform_initial_state()