AlgoParamsDEMAP {DEBBI}

AlgoParamsDEMAP

Description

get control parameters for DEMAP function

Usage

AlgoParamsDEMAP(
  n_params,
  n_chains = NULL,
  n_iter = 1000,
  init_sd = 0.01,
  init_center = 0,
  n_cores_use = 1,
  step_size = NULL,
  jitter_size = 1e-06,
  crossover_rate = 1,
  parallel_type = "none",
  return_trace = FALSE,
  thin = 1
)

Arguments

n_params

number of free parameters estimated

n_chains

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

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 that 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).

jitter_size

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

crossover_rate

number on the interval (0,1]. Determines the probability a parameter on a chain is updated on a given crossover step, sampled from a Bernoulli distribution.

parallel_type

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

return_trace

logical, if true, function returns particle trajectories. This is helpful for diagnosing convergence or debugging model code. Function will return an iteration/thin $x$ n_chains $x$ n_params array and the estimated ELBO of each particle in a iteration/thin x n_chains array.

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 DEMAP function