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 )
model |
An instance of |
batch_shape |
Batch shape ( |
seed |
integer to seed the random number generator. |
name |
string prefixed to ops created by this function.
Default 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.
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()
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