This function proposes an MCMC candidate variable, and either accepts or rejects the candidate

This function receives a current MCMC variable, then calculates a possible successor and returns it in the case of acceptance. It returns the (old) current state upon rejection of the candidate.

Usage

mcmcUpdate(
  simulate,
  experiments,
  model,
  logLikelihood,
  dprior,
  gradLogLikelihood = NULL,
  gprior = NULL,
  fisherInformation = NULL,
  fisherInformationPrior = NULL
)

Arguments

simulate

a function that simulates the model for a given parMCMC

experiments

the list of experiments (with simulation instructions)

model

the list of model functions

logLikelihood

a function that calculates log-likelihood values for given parMCMC

dprior

prior density function

gradLogLikelihood

a function that calculates the gradient of the log-likelihood for given parMCMC

gprior

gradient of the prior density

fisherInformation

a function that calculates approximates Fisher information matrices

fisherInformationPrior

a constant matrix, the prior distributions fisher information

Value

a function that returns possibly updated states of the Markov chain

Details

The Markov chain has a current state (the MCMC variable, often x in literature), but in the context of sampling the MCMC variables are used as the parameters to a scientific model of some sort (and these often have state variables, also x, or y). This is why we call the variables parMCMC (parABC), or parCurrent|Given|Proposal, and similar.