Initialize the Markov chain

This function must append all required attributes to the MCMC varible, for the Markov chain to update correctly.

Usage

mcmc_init(
  beta,
  parMCMC,
  simulate,
  logLikelihood = ll,
  dprior = function(x) prod(rnorm(x)),
  gradLogLikelihood = NULL,
  gprior = NULL,
  fisherInformation = NULL
)

Arguments

beta

inverse temperature for the Markov chain (parallel tempering)

parMCMC

a plain starting value for the Markov chain

simulate

a closure that maps the MCMC variable to simulation results (the simulation experiments are enclosed in this function).

logLikelihood

a function that maps simulations to logLikelihood values

dprior

density of the prior distribution

gradLogLikelihood

the gradient function of the logLikelihood (optional) – only if the algorithm requires it

gprior

the gradient pf the log-prior (for SMMALA and similar algorithms).

fisherInformation

a function that calculates the Fisher Information matrix

Value

the same starting parameter vector, but with attributes.

Examples

m <- model_from_tsv(uqsa_example("AKAR4"))
o <- as_ode(m)
p0 <- values(m$Parameter)
c_path(o) <- write_c_code(generate_code(o))
so_path(o) <- shlib(o)
ex <- experiments(m,o)
s <- simfi(ex,o)
dprior <- dNormalPrior(p0,m$Parameter$stdv)
p <- mcmc_init(1.0,p0,s,dprior=dprior)
print(names(attributes(p))) ## now has attributes necessary for MCMC
#> [1] "names"         "unit"          "beta"          "simulations"  
#> [5] "prior"         "logLikelihood" "class"