MCMC_component: Componentwise Adapted Metropolis Hastings Sampler

In seeds: Estimate Hidden Inputs using the Dynamic Elastic Net

Description

Algorithm implemented according to Engelhardt et al. 2017.

Usage

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
)

Arguments

LOGLIKELIHOOD_func	likelihood function
STEP_SIZE	number of samples per mcmc step. This should be greater than numberStates*500.Values have direct influence on the runtime.
STEP_SIZE_INNER	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.
EPSILON	vector of hidden influences (placeholder for customized version)
JUMP_SCALE	ODE system
STEP	time step of the sample algorithm corresponding to the given vector of time points
OBSERVATIONS	observed state dynamics e.g. protein concentrations
Y0	initial values of the system
INPUTDATA	discrete input function e.g. stimuli
PARAMETER	model parameters estimates
EPSILON_ACT	vector of current hidden influences
SIGMA	current variance of the prior for the hidden influences (calculated during the Gibbs update)
DIAG	diagonal weight matrix of the current Gibbs step
GIBBS_par	GIBBS_PAR[["BETA"]] and GIBBS_PAR[["ALPHA"]]; prespecified or calculated vector of state weights
N	number of system states
BURNIN	number of dismissed samples during burn-in
objective	objective function

Details

The function can be replaced by an user defined version if necessary

Value

A matrix with the sampled hidden inputs (row-wise)

seeds documentation built on July 14, 2020, 1:07 a.m.