High Level Metropolis function

This function uses default assumption everywhere and returns a function that will sample from the given model. This funciton will generate code, compile the code, create an ODE solver for it, infer the sampling space from the scale of the parameters, create all necessary functions to move in parameter space (gradients of likelihood and prior), as well as Fisher Information functions.

Usage

high_level_metropolis(
  m,
  o = as_ode(m, cla = FALSE),
  ex = experiments(m, o),
  x = values(m$Parameter),
  beta = 1
)

Arguments

m

the model's TSV representation read via model_from_tsv

o

(optional) ode representation of m

ex

experiments of m, with simulation instructions for o.

x

initial point of the markov chain, pre in itialized to have the right attributes.

beta

for parallel tempering, the log-likelihood will have a factor of beta applied to it

Value

smmala a function of three arguments: p0, N, eps; where p0 is the starting point, N is the desired sample-size, and eps is the step size. This function has an attribute called "init", with a pre-initialized starting point.

Examples

# \donttest{
m <- model_from_tsv(uqsa_example("AKAP79"))
rwm <- high_level_metropolis(m) # "random walk", metropolis algorithm
#> The parameters are given in log10-scale, so the simulator will do the reverse transformation: 10^p.
p <- rwm %@% "init"             # a valid starting point
N <- 200
smallSample <- rwm(rwm %@% "init",N,1e-6)
plot(
  smallSample %@% "logLikelihood",
  type="l",
  main=sprintf("%i iterations",N),
  xlab="iterations",
  ylab="log-likelihood"
)

# }