class_docstr: Results from applying the Kalman smoother and/or filter to a state space
  model.
  
  Parameters
  ----------
  model : Representation
      A Statespace representation
  simulation_smoother : {{prefix}}SimulationSmoother object
      The Cython simulation smoother object with which to simulation smooth.
  random_state : {None, int, Generator, RandomState}, optional
      If `seed` is None (or `np.random`), the `numpy.random.RandomState`
      singleton is used.
      If `seed` is an int, a new ``numpy.random.RandomState`` instance
      is used, seeded with `seed`.
      If `seed` is already a ``numpy.random.Generator`` or
      ``numpy.random.RandomState`` instance then that instance is used.
  
  Attributes
  ----------
  model : Representation
      A Statespace representation
  dtype : dtype
      Datatype of representation matrices
  prefix : str
      BLAS prefix of representation matrices
  simulation_output : int
      Bitmask controlling simulation output.
  simulate_state : bool
      Flag for if the state is included in simulation output.
  simulate_disturbance : bool
      Flag for if the state and observation disturbances are included in
      simulation output.
  simulate_all : bool
      Flag for if simulation output should include everything.
  generated_measurement_disturbance : ndarray
      Measurement disturbance variates used to genereate the observation
      vector.
  generated_state_disturbance : ndarray
      State disturbance variates used to genereate the state and
      observation vectors.
  generated_obs : ndarray
      Generated observation vector produced as a byproduct of simulation
      smoothing.
  generated_state : ndarray
      Generated state vector produced as a byproduct of simulation smoothing.
  simulated_state : ndarray
      Simulated state.
  simulated_measurement_disturbance : ndarray
      Simulated measurement disturbance.
  simulated_state_disturbance : ndarray
      Simulated state disturbance.
docstr: Random draw of the measurement disturbance vector from its conditional
  distribution.
  
  Notes
  -----
  
  .. math::
  
      \varepsilon ~ N(\hat \varepsilon, Var(\hat \varepsilon \mid Y_n))
docstr_tok: ["Random","draw","of","the","measurement","disturbance","vector","from","its","conditional","distribution",".","Notes","--","--","-","..","math",":",":","\\varepsilon","~","N","(","\\hat","\\varepsilon",",","Var","(","\\hat","\\varepsilon","\\mid","Y_n",")",")"]
code: def simulated_measurement_disturbance(self):
      r"""
      Random draw of the measurement disturbance vector from its conditional
      distribution.
  
      Notes
      -----
  
      .. math::
  
          \varepsilon ~ N(\hat \varepsilon, Var(\hat \varepsilon \mid Y_n))
      """
      if self._simulated_measurement_disturbance is None:
          self._simulated_measurement_disturbance = np.array(
              self._simulation_smoother.simulated_measurement_disturbance,
              copy=True,
          )
      return self._simulated_measurement_disturbance
  
imports: import numbers
  import warnings
  import numpy
  from .kalman_smoother import KalmanSmoother
  from .cfa_simulation_smoother import CFASimulationSmoother
  from .None import tools
label_desc: Use image node_id 15 for calling the SimulationSmoothResults obj's underlying member method code with example usage: obj.simulated_measurement_disturbance() and returns: self