Algorithm implemented according to Engelhardt et al. 2017.
MCMC_component(
LOGLIKELIHOOD_func,
STEP_SIZE,
STEP_SIZE_INNER,
EPSILON,
JUMP_SCALE,
STEP,
OBSERVATIONS,
Y0,
INPUTDATA,
PARAMETER,
EPSILON_ACT,
SIGMA,
DIAG,
GIBBS_par,
N,
BURNIN,
objective
)likelihood function
number of samples per mcmc step. This should be greater than numberStates*500.Values have direct influence on the runtime.
number of inner samples. This should be greater 15 to guarantee a reasonable exploration of the sample space. Values have direct influnce on the runtime.
vector of hidden influences (placeholder for customized version)
ODE system
time step of the sample algorithm corresponding to the given vector of time points
observed state dynamics e.g. protein concentrations
initial values of the system
discrete input function e.g. stimuli
model parameters estimates
vector of current hidden influences
current variance of the prior for the hidden influences (calculated during the Gibbs update)
diagonal weight matrix of the current Gibbs step
GIBBS_PAR[["BETA"]] and GIBBS_PAR[["ALPHA"]]; prespecified or calculated vector of state weights
number of system states
number of dismissed samples during burn-in
objective function
A matrix with the sampled hidden inputs (row-wise)
The function can be replaced by an user defined version if necessary