penalized_likelihood: Penalized likelihood objective function

In ntthung/ldsr: Linear Dynamical System Reconstruction

Description

Kalman_smoother returns X and likelihood. The penalized likelihood is the likelihood minus the sum-of-squares of the measurement update. This is used as the fitness function in genetic algorihm.

Usage

penalized_likelihood(y, u, v, theta.vec, lambda)

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)
theta.vec	a vector of parameter elements (i.e, the vectorized version of theta in Kalman_smoother)
lambda	weight of the penalty

Value

The penalized likelihood (a real number)

ntthung/ldsr documentation built on Aug. 28, 2020, 2:34 a.m.