kernel: Markov kernel

In jeremyhengjm/UnbiasedScore: Unbiased estimators for partially observed diffusions

Markov kernel

Description

Runs a Markov kernel that leaves smoothing distribution invariant.

Usage

kernel(
  model,
  theta,
  discretization,
  observations,
  nparticles,
  resampling_threshold = 1,
  ref_trajectory = NULL,
  algorithm = "CPF",
  treestorage = FALSE
)

Arguments

model	a list representing a hidden Markov model, e.g. hmm_ornstein_uhlenbeck
theta	a vector of parameters as input to model functions
discretization	list containing stepsize, nsteps, statelength and obstimes
observations	a matrix of observations, of size nobservations x ydimension
nparticles	number of particles
resampling_threshold	ESS proportion below which resampling is triggered (always resample at observation times by default)
ref_trajectory	a matrix of reference trajectory, of size xdimension x statelength; if missing, this function runs a standard particle filter
algorithm	character specifying type of algorithm desired, i.e. CPF for conditional particle filter, CASPF for conditional ancestor sampling particle filter, CBSPF for conditional backward sampling particle filter
treestorage	logical specifying tree storage of Jacob, Murray and Rubenthaler (2013); if missing, this function store all states and ancestors

Value

a matrix containing a new trajectory of size xdimension x statelength.

jeremyhengjm/UnbiasedScore documentation built on Nov. 17, 2023, 1:48 a.m.