viking: Viking: Variational bayesIan variance tracKING

In viking: State-Space Models Inference by Kalman or Viking

Viking: Variational bayesIan variance tracKING

Description

viking is the state-space estimation realized by Viking, generalizing the Kalman Filter to variance uncertainty.

Usage

viking(
  X,
  y,
  theta0,
  P0,
  hata0,
  s0,
  hatb0,
  Sigma0,
  n_iter = 2,
  mc = 10,
  rho_a = 0,
  rho_b = 0,
  learn_sigma = TRUE,
  learn_Q = TRUE,
  K = NULL,
  mode = "diagonal",
  thresh = TRUE,
  phi = logt,
  phi1 = logt1,
  phi2 = logt2
)

Arguments

X	the explanatory variables
y	the time series
theta0	initial theta
P0	initial P
hata0	initial hata
s0	initial s
hatb0	initial hatb
Sigma0	initial Sigma
n_iter	(optional, default 2) number of alternate steps
mc	(optional, default 10) number of Monte-Carlo samples
rho_a	(optional, default 0) learning rate of a
rho_b	(optional, default 0) learning rate of b
learn_sigma	(optional, default TRUE) asserts the estimation of a
learn_Q	(optional, default TRUE) asserts the estimation of b
K	(optional, default NULL) if not NULL then it is a multiplicative factor of the state in the state update
mode	(optional, default 'diagonal')
thresh	(optional, default TRUE)
phi	(optional, default logt)
phi1	(optional, default logt1)
phi2	(optional, default logt2)

Value

a list composed of the evolving value of all the parameters: theta_arr, P_arr, q_arr, hata_arr, s_arr, hatb_arr, Sigma_arr

References

J. de Vilmarest, O. Wintenberger (2021), Viking: Variational Bayesian Variance Tracking. <arXiv:2104.10777>

viking documentation built on Oct. 6, 2023, 5:06 p.m.