progressive_fusion_mixG | R Documentation |
(Progressive) D&C Monte Carlo Fusion with mixture Gaussian target
progressive_fusion_mixG( N_schedule, time_schedule, base_samples, n_comp, weights, means, sds, start_beta, precondition = TRUE, bounds_multiplier = 1.1, seed = NULL, n_cores = parallel::detectCores() )
N_schedule |
vector of length (L-1), where N_schedule[l] is the number of samples per node at level l |
time_schedule |
vector of length(L-1), where time_schedule[l] is the time chosen for Fusion at level l |
base_samples |
list of length (1/start_beta), where samples_to_fuse[c] contains the samples for the c-th node in the level |
n_comp |
integer number of components of mixture Gaussian |
weights |
vector: weights of mixture Gaussian |
means |
vector: means of mixture Gaussian |
sds |
vector: st.devs of mixture Gaussian |
start_beta |
beta for the base level |
precondition |
either a logical value to determine if preconditioning values are used (TRUE - and is set to be the variance of the sub-posterior samples) or not (FALSE - and is set to be 1 for all sub-posteriors), or a list of length (1/start_beta) where precondition[[c]] is the preconditioning value for sub-posterior c. Default is TRUE |
bounds_multiplier |
scalar value to multiply bounds by (should greater than or equal to 1) |
seed |
seed number - default is NULL, meaning there is no seed |
n_cores |
number of cores to use |
A list with components:
list of length (L-1), where samples[[l]][[i]] are the samples for level l, node i
list of length (L-1), where time[[l]][[i]] is the run time for level l, node i
list of length (L-1), where rho_acc[[l]][i] is the acceptance rate for first fusion step for level l, node i
list of length (L-1), where Q_acc[[l]][i] is the acceptance rate for second fusion step for level l, node i
list of length (L-1), where rhoQ_acc[[l]][i] is the overall acceptance rate for fusion for level l, node i
vector of length (L-1), where diffusion_times[l] are the times for fusion in level l (= time_schedule)
preconditioning values used in the algorithm for each node
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