Mstep {ldsr}

Maximizing expected likelihood using analytical solution

Description

Maximizing expected likelihood using analytical solution

Usage

Mstep(y, u, v, fit)

Arguments

y

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

u

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

v

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

fit

result of Kalman_smoother

Value

An object of class theta: a list of