Description Usage Arguments Value Examples
View source: R/seq_fusion_exp_4.R
Sequential Time-adapting Monte Carlo Fusion with base level of the form exp(-((x^4)*beta)/2)
1 2 | parallel_sequential_fusion_TA_exp_4_rcpp(N_schedule, mean = 0, global_T,
start_beta, base_samples, seed = NULL)
|
N_schedule |
vector of length (L-1), where N_schedule[l] is the number of samples per node at level l |
mean |
mean value |
global_T |
time T for time-adapting fusion algorithm |
start_beta |
beta for the base level |
base_samples |
list of length (1/start_beta), where samples_to_fuse[c] containg the samples for the c-th node in the level |
seed |
seed number - default is NULL, meaning there is no seed |
samples: samples from Sequential fusion
time: vector of length (L-1), where time[l] is the run time for level l
rho_acc: vector of length (L-1), where rho_acc[l] is the acceptance rate for first fusion step for level l
Q_acc: vector of length (L-1), where Q_acc[l] is the acceptance rate for second fusion step for level l
input_betas: list of length (L), where input_betas[[l]] is the input betas for level l
output_beta: vector of length(L-1), where output_beta[l] is the beta for level l
diffusion_times: list of length (L-1), where diffusion_times[[l]] are the scaled/adapted times for fusion in level l
1 2 3 4 5 6 | input_samples <- base_rejection_sampler_exp_4(beta = 1/4, nsamples = 100000, proposal_mean = 0, proposal_sd = 1.5, dominating_M = 1.4)
test <- parallel_sequential_fusion_TA_exp_4_rcpp(N_schedule = rep(10000, 3), global_T = 0.5,
start_beta = 1/4, base_samples = input_samples)
# plot results
plot_levels_seq_exp_4(test, from = -3, to = 3, plot_rows = 2, plot_columns = 2)
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