get control parameters for DEMAP function
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
)number of free parameters estimated
number of particle chains, 3*n_params is the default value
number of iterations to run the sampling algorithm, 1000 is default
positive scalar or n_params-dimensional numeric vector, determines the standard deviation of the Gaussian initialization distribution
scalar or n_params-dimensional numeric vector that determines the mean of the Gaussian initialization distribution
number of cores used when using parallelization.
positive scalar, jump size in DE crossover step, default is 2.38/sqrt(2*n_params).
positive scalar, noise is added during crossover step from Uniform(-jitter_size,jitter_size) distribution. 1e-6 is the default value.
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.
string specifying parallelization type. 'none','FORK', or 'PSOCK' are valid values. 'none' is default value.
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.
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.
list of control parameters for the DEMAP function