rts
===

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


Functions
---------

.. autoapisummary::

   smolgp.solvers.integrated.rts.IntegratedRTSSmoother
   smolgp.solvers.integrated.rts.integrated_rts_smoother


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

.. py:function:: IntegratedRTSSmoother(kernel, t_states, obsid, instid, stateid, kalman_results)

   Wrapper for jitted integrated_rts_smoother function

   :param kernel: IntegratedStateSpaceModel kernel
   :param t_states: Array of size K, sorted time coordinate of all states (exposure starts and ends)
   :param obsid: Array of size N, which observation (0,...,N-1) is being made at each state k
   :param instid: Array of size N, which instrument (0,...,Ninst-1) recorded observation n
   :param stateid: Array of size K, 0 for exposure-start, 1 for exposure-end
   :param kalman_results: output from Kalman filter (m_filtered, P_filtered, m_predicted, P_predicted)

   :returns: filtered means
             P_filtered: filtered covariances
             m_predicted: predicted means
             P_predicted: predicted covariances
   :rtype: m_filtered


.. py:function:: integrated_rts_smoother(A_aug, RESET, t_states, obsid, instid, stateid, m_filtered, P_filtered, m_predicted, P_predicted)

   Jax implementation of the integrated RTS smoothing algorithm

   See Section 3.2.2 in Rubenzahl & Hattori et al. (in prep)
   for detailed description of the algorithm and notation.