tensorflow_probability/python/sts/structural_time_series.py
def make_state_space_model(self,
                             num_timesteps,
                             param_vals=None,
                             initial_state_prior=None,
                             initial_step=0):
    """Instantiate this model as a Distribution over specified `num_timesteps`.

    Args:
      num_timesteps: Python `int` number of timesteps to model.
      param_vals: a list of `Tensor` parameter values in order corresponding to
        `self.parameters`, or a dict mapping from parameter names to values.
      initial_state_prior: an optional `Distribution` instance overriding the
        default prior on the model's initial state. This is used in forecasting
        ("today's prior is yesterday's posterior").
      initial_step: optional `int` specifying the initial timestep to model.
        This is relevant when the model contains time-varying components,
        e.g., holidays or seasonality.

    Returns:
      dist: a `LinearGaussianStateSpaceModel` Distribution object.
    """
    return self._make_state_space_model(
        num_timesteps=num_timesteps,
        param_map=self._canonicalize_param_vals_as_map(param_vals),
        initial_state_prior=initial_state_prior,
        initial_step=initial_step)
def make_state_space_model(self,
                             num_timesteps,
                             param_vals=None,
                             initial_state_prior=None,
                             initial_step=0):
    """Instantiate this model as a Distribution over specified `num_timesteps`.

    Args:
      num_timesteps: Python `int` number of timesteps to model.
      param_vals: a list of `Tensor` parameter values in order corresponding to
        `self.parameters`, or a dict mapping from parameter names to values.
      initial_state_prior: an optional `Distribution` instance overriding the
        default prior on the model's initial state. This is used in forecasting
        ("today's prior is yesterday's posterior").
      initial_step: optional `int` specifying the initial timestep to model.
        This is relevant when the model contains time-varying components,
        e.g., holidays or seasonality.

    Returns:
      dist: a `LinearGaussianStateSpaceModel` Distribution object.
    """
    return self._make_state_space_model(
        num_timesteps=num_timesteps,
        param_map=self._canonicalize_param_vals_as_map(param_vals),
        initial_state_prior=initial_state_prior,
        initial_step=initial_step)
Instantiate this model as a Distribution over specified `num_timesteps`.

    Args:
      num_timesteps: Python `int` number of timesteps to model.
      param_vals: a list of `Tensor` parameter values in order corresponding to
        `self.parameters`, or a dict mapping from parameter names to values.
      initial_state_prior: an optional `Distribution` instance overriding the
        default prior on the model's initial state. This is used in forecasting
        ("today's prior is yesterday's posterior").
      initial_step: optional `int` specifying the initial timestep to model.
        This is relevant when the model contains time-varying components,
        e.g., holidays or seasonality.

    Returns:
      dist: a `LinearGaussianStateSpaceModel` Distribution object.