AlgoParamsDEMCMC: AlgoParamsDEMCMC

In DEBBI: Differential Evolution-Based Bayesian Inference

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

DEBBI documentation built on May 17, 2022, 5:08 p.m.