logLik.lineargaussian | R Documentation |

`bssm_model`

Computes the log-likelihood of a state space model defined by `bssm`

package.

## S3 method for class 'lineargaussian' logLik(object, ...) ## S3 method for class 'nongaussian' logLik( object, particles, method = "psi", max_iter = 100, conv_tol = 1e-08, seed = sample(.Machine$integer.max, size = 1), ... ) ## S3 method for class 'ssm_nlg' logLik( object, particles, method = "bsf", max_iter = 100, conv_tol = 1e-08, iekf_iter = 0, seed = sample(.Machine$integer.max, size = 1), ... ) ## S3 method for class 'ssm_sde' logLik( object, particles, L, seed = sample(.Machine$integer.max, size = 1), ... )

`object` |
Model of class |

`...` |
Ignored. |

`particles` |
Number of samples for particle filter
(non-negative integer). If 0, approximate log-likelihood is returned either
based on the Gaussian approximation or EKF, depending on the |

`method` |
Sampling method. For Gaussian and non-Gaussian models with
linear dynamics,options are |

`max_iter` |
Maximum number of iterations used in Gaussian approximation, as a positive integer. Default is 100 (although typically only few iterations are needed). |

`conv_tol` |
Positive tolerance parameter used in Gaussian approximation. Default is 1e-8. |

`seed` |
Seed for the C++ RNG (positive integer). |

`iekf_iter` |
Non-negative integer. If zero (default), first
approximation for non-linear Gaussian models is obtained from extended
Kalman filter. If |

`L` |
Integer defining the discretization level defined as (2^L). |

A numeric value.

Durbin, J., & Koopman, S. (2002). A Simple and Efficient Simulation Smoother for State Space Time Series Analysis. Biometrika, 89(3), 603-615.

Shephard, N., & Pitt, M. (1997). Likelihood Analysis of Non-Gaussian Measurement Time Series. Biometrika, 84(3), 653-667.

Gordon, NJ, Salmond, DJ, Smith, AFM (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F, 140, 107-113.

Vihola, M, Helske, J, Franks, J. Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. Scand J Statist. 2020; 1-38. https://doi.org/10.1111/sjos.12492

Van Der Merwe, R, Doucet, A, De Freitas, N, Wan, EA (2001). The unscented particle filter. In Advances in neural information processing systems, p 584-590.

Jazwinski, A 1970. Stochastic Processes and Filtering Theory. Academic Press.

Kitagawa, G (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5, 1-25.

particle_smoother

model <- ssm_ulg(y = c(1,4,3), Z = 1, H = 1, T = 1, R = 1) logLik(model) model <- ssm_ung(y = c(1,4,3), Z = 1, T = 1, R = 0.5, P1 = 2, distribution = "poisson") model2 <- bsm_ng(y = c(1,4,3), sd_level = 0.5, P1 = 2, distribution = "poisson") logLik(model, particles = 0) logLik(model2, particles = 0) logLik(model, particles = 10, seed = 1) logLik(model2, particles = 10, seed = 1)

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