rts

rts#

Functions#

RTSSmoother(kernel, X, kalman_results)

Wrapper for RTS smoother

rts_smoother(A, t, m_filtered, P_filtered, ...)

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

Module Contents#

smolgp.solvers.rts.RTSSmoother(kernel, X, kalman_results)[source]#

Wrapper for RTS smoother

Parameters:

  • kernel – StateSpaceModel kernel

  • X – data coordinates, e.g. time or (time, texp, instid)

  • kalman_results – output from Kalman filter (m_filtered, P_filtered, m_predicted, P_predicted)

Returns:

smoothed means P_smooth: smoothed covariances

Return type:

m_smooth

smolgp.solvers.rts.rts_smoother(A, t, m_filtered, P_filtered, m_predicted, P_predicted)[source]#

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.