Run Metropolis-Hastings algorithm for a DLM, using the kalman filter to calculate the likelihood
Particle Marginal Metropolis Hastings for a ContinuousTime Model Where the log-likelihood is an estimate calculated using the bootstrap particle filter
Particle Marginal Metropolis Hastings for a ContinuousTime Model Where the log-likelihood is an estimate calculated using the bootstrap particle filter
a DGLM model
an array of observations
the intial parameters to start the Markov Chain
the number of particles in the PF
a Markov Chain Process which can be drawn from