sts_build_factored_surrogate_posterior {tfprobability}

Build a variational posterior that factors over model parameters.

Description

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.

Usage

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()