approximate_discretized_score: MCMC estimator of score function at a discretization level

In jeremyhengjm/UnbiasedScore: Unbiased estimators for partially observed diffusions

MCMC estimator of score function at a discretization level

Description

Computes MCMC estimator of the time-discretized score function.

Usage

approximate_discretized_score(
  model,
  theta,
  discretization,
  observations,
  nparticles,
  resampling_threshold = 1,
  initialization = "particlefilter",
  algorithm = "CPF",
  max_iterations
)

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 terminal_time x ydimension
nparticles	number of particles
resampling_threshold	ESS proportion below which resampling is triggered (always resample at observation times by default)
initialization	choice of distribution to initialize chains, such as dynamics or the default particlefilter
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
max_iterations	number of MCMC iterations

Value

a list with objects such as: mcmcestimator is the MCMC estimator of the discretized score; cost is the cost of the algorithm; elapsedtime is the time taken by the algorithm.

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