AlgoParamsDEMCMC {DEBBI}

AlgoParamsDEMCMC

Description

AlgoParamsDEMCMC

Usage

AlgoParamsDEMCMC(
  n_params,
  n_chains = NULL,
  param_names = NULL,
  n_iter = 1000,
  init_sd = 0.01,
  init_center = 0,
  n_cores_use = 1,
  step_size = NULL,
  jitter_size = 1e-06,
  parallel_type = "none",
  burnin = 0,
  thin = 1
)

Arguments

n_params

number of free parameters estimated

n_chains

number of MCMC chains, 3*n_params is the default value

param_names

optional vector of parameter names

n_iter

number of iterations to run the sampling algorithm, 1000 is default

init_sd

positive scalar or n_params-dimensional numeric vector, determines the standard deviation of the Gaussian initialization distribution

init_center

scalar or n_params-dimensional numeric vector, determines the mean of the Gaussian initialization distribution

n_cores_use

number of cores used when using parallelization.

step_size

positive scalar, jump size in DE crossover step, default is 2.38/sqrt(2*n_params) which is optimal for multivariate Gaussian target distribution (ter Braak, 2006)

jitter_size

positive scalar, noise is added during crossover step from Uniform(-jitter_size,jitter_size) distribution. 1e-6 is the default value.

parallel_type

string specifying parallelization type. 'none','FORK', or 'PSOCK' are valid values. 'none' is default value.

burnin

number of initial iterations to discard. Default value is 0.

thin

positive integer, only every 'thin'-th iteration will be stored. Default value is 1. Increasing thin will reduce the memory required, while running chains for longer.

Value

list of control parameters for the DEMCMC function