logLik.ssm_sde

object

Number of samples for particle filter. If 0, 
approximate log-likelihood is returned either based on the gaussian approximation or EKF, 
depending on the <code>method</code> argument.

nsim

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

Seed for the random number generator.

seed

Computes the log-likelihood of the state space model of <code>bssm</code> package.

Efficient methods for Bayesian inference of state space models
via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel
importance sampling type weighted estimators
(Vihola, Helske, and Franks, 2020, <arXiv:1609.02541>).
Gaussian, Poisson, binomial, negative binomial, and Gamma
observation densities and basic stochastic volatility models with Gaussian state
dynamics, as well as general non-linear Gaussian models and discretised
diffusion models are supported.

logLik.ssm_sde: Log-likelihood of a State Space Model with SDE dynamics

Description

Usage

Arguments