Auxiliary for Controlling Particle Fitting

Auxiliary for additional settings with PF_EM.

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)

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. NULL implies no additional error term.


FALSE if 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_smooth particles 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.


tolerance passed to nloptr in mode approximation.


index to start averaging. Values less then or equal to zero yields no averaging.


The method argument can take the following values

  • bootstrap_filter for a bootstrap filter.

  • PF_normal_approx_w_cloud_mean for 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_mean for an auxiliary particle filter version of PF_normal_approx_w_cloud_mean.

  • PF_normal_approx_w_particles for a filter similar to PF_normal_approx_w_cloud_mean and differs by making a Taylor approximation at a mean given each sampled parent and/or child particle.

  • AUX_normal_approx_w_particles for an auxiliary particle filter version of PF_normal_approx_w_particles.

The smoother argument can take the following values

  • Fearnhead_O_N for the smoother in Fearnhead, Wyncoll, and Tawn (2010).

  • Brier_O_N_square for 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.

See Also


  • PF_control
Documentation reproduced from package dynamichazard, version 0.6.5, License: GPL-2

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