rts
===

.. py:module:: smolgp.solvers.rts


Functions
---------

.. autoapisummary::

   smolgp.solvers.rts.RTSSmoother
   smolgp.solvers.rts.rts_smoother


Module Contents
---------------

.. py:function:: RTSSmoother(kernel, X, kalman_results)

   Wrapper for RTS smoother

   :param kernel: StateSpaceModel kernel
   :param X: data coordinates, e.g. time or (time, texp, instid)
   :param kalman_results: output from Kalman filter (m_filtered, P_filtered, m_predicted, P_predicted)

   :returns: smoothed means
             P_smooth: smoothed covariances
   :rtype: m_smooth


.. py:function:: rts_smoother(A, t, m_filtered, P_filtered, m_predicted, P_predicted)

   Jax implementation of the Rauch-Tung-Striebel (RTS) smoothing algorithm

   See Theorem 8.2 (pdf page 156) in "Bayesian Filtering and Smoothing"
   by Simo Särkkä for detailed description of the algorithm and notation.