Auxiliary for Controlling Particle Fitting
Auxiliary for additional settings with
PF_control(N_fw_n_bw = NULL, N_smooth = NULL, N_first = NULL, eps = 0.01, forward_backward_ESS_threshold = NULL, method = "AUX_normal_approx_w_cloud_mean", n_max = 25, n_threads = getOption("ddhazard_max_threads"), smoother = "Fearnhead_O_N", Q_tilde = NULL, est_a_0 = TRUE, N_smooth_final = N_smooth, nu = 0L, covar_fac = -1, ftol_rel = 1e-08, averaging_start = -1L, fix_seed = TRUE)
number of particles to use in forward and backward filter.
number of particles to use in particle smoother.
number of particles to use at time \(0\) and time \(d + 1\).
convergence threshold in EM method.
required effective sample size to not re-sample in the particle filters.
method for forward, backward and smoothing filter.
maximum number of iterations of the EM algorithm.
maximum number threads to use in the computations.
smoother to use.
covariance matrix of additional error term to add to the proposal distributions.
NULLimplies no additional error term.
FALSEif the starting value of the state model should be fixed. Does not apply for
type = "VAR".
number of particles to sample with replacement from the smoothed particle cloud with
N_smoothparticles using the particles' weights. This causes additional sampling error but decreases the computation time in the M-step.
integer with degrees of freedom to use in the (multivariate) t-distribution used as the proposal distribution. A (multivariate) normal distribution is used if it is zero.
factor to scale the covariance matrix with. Ignored if the values is less than or equal to zero.
relative convergence tolerance of the mode objective in mode approximation.
index to start averaging. Values less then or equal to zero yields no averaging.
TRUEif the same seed should be used. E.g., in
PF_EMthe same seed will be used in each iteration of the E-step of the MCEM algorithm.
method argument can take the following values
bootstrap_filterfor a bootstrap filter.
PF_normal_approx_w_cloud_meanfor a particle filter where a Gaussian approximation is used using a Taylor approximation made at the mean for the current particle given the mean of the parent particles and/or mean of the child particles.
AUX_normal_approx_w_cloud_meanfor an auxiliary particle filter version of
PF_normal_approx_w_particlesfor a filter similar to
PF_normal_approx_w_cloud_meanand differs by making a Taylor approximation at a mean given each sampled parent and/or child particle.
AUX_normal_approx_w_particlesfor an auxiliary particle filter version of
smoother argument can take the following values
Fearnhead_O_Nfor the smoother in Fearnhead, Wyncoll, and Tawn (2010).
Brier_O_N_squarefor the smoother in Briers, Doucet, and Maskell (2010).
A list with components named as the arguments.
Gordon, N. J., Salmond, D. J., and Smith, A. F. (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In IEE Proceedings F (Radar and Signal Processing), (Vol. 140, No. 2, pp. 107-113). IET Digital Library.
Pitt, M. K., and Shephard, N. (1999) Filtering via simulation: Auxiliary particle filters. Journal of the American statistical association, 94(446), 590-599.
Fearnhead, P., Wyncoll, D., and Tawn, J. (2010) A sequential smoothing algorithm with linear computational cost. Biometrika, 97(2), 447-464.
Briers, M., Doucet, A., and Maskell, S. (2010) Smoothing algorithms for state-space models. Annals of the Institute of Statistical Mathematics, 62(1), 61.