Initialize from a uniform [-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
)

Arguments

parameter	sts$Parameter named tuple instance.
return_constrained	if TRUE, re-applies the constraining bijector to return initializations in the original domain. Otherwise, returns initializations in the unconstrained space. Default value: TRUE.
init_sample_shape	sample_shape of the sampled initializations. Default value: list().
seed	integer to seed the random number generator.

Value

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).

See also

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()