[-2, 2] distribution in unconstrained space.Initialize from a uniform [-2, 2] distribution in unconstrained space.
sts_sample_uniform_initial_state(
parameter,
return_constrained = TRUE,
init_sample_shape = list(),
seed = NULL
)uniform_initializer Tensor of shape
concat([init_sample_shape, parameter.prior.batch_shape, transformed_event_shape]), where
transformed_event_shape is parameter.prior.event_shape, if
return_constrained=TRUE, and otherwise it is
parameter$bijector$inverse_event_shape(parameter$prior$event_shape).
sts$Parameter named tuple instance.
if TRUE, re-applies the constraining bijector
to return initializations in the original domain. Otherwise, returns
initializations in the unconstrained space.
Default value: TRUE.
sample_shape of the sampled initializations.
Default value: list().
integer to seed the random number generator.
Other sts-functions:
sts_build_factored_surrogate_posterior(),
sts_build_factored_variational_loss(),
sts_decompose_by_component(),
sts_decompose_forecast_by_component(),
sts_fit_with_hmc(),
sts_forecast(),
sts_one_step_predictive()