Mstep

Observation matrix (may need to be normalized and centered before hand) (q rows, T columns)

Input matrix for the state equation (m_u rows, T columns)

Input matrix for the output equation (m_v rows, T columns)

result of <a rd-options="" href="/link/Kalman_smoother?package=ldsr&version=0.0.2" data-mini-rdoc="ldsr::Kalman_smoother">Kalman_smoother</a>

Maximizing expected likelihood using analytical solution

Streamflow (and climate) reconstruction using Linear Dynamical Systems.
The advantage of this method is the additional state trajectory which can reveal more information
about the catchment or climate system. For details of the method please refer to Nguyen and Galelli
(2018) <doi:10.1002/2017WR022114>.

Hung Nguyen

ldsr

Linear Dynamical System Reconstruction

Stefano Galelli

Mstep function

result of <a rd-options='' href='Kalman_smoother'>Kalman_smoother</a>

Mstep: Maximizing expected likelihood using analytical solution

Description

Usage

Arguments

Value