parallel_sequential_fusion_TA_exp_4: Time-adapting Sequential Monte Carlo Fusion

In rchan26/exp4Tempering: Hierarchical and Sequential Monte Carlo Fusion for exp(-(x^4)/2)

Description

Sequential Time-adapting Monte Carlo Fusion with base level of the form exp(-((x^4)*beta)/2)

Usage

parallel_sequential_fusion_TA_exp_4(N_schedule, mean = 0, global_T,
  start_beta, base_samples, study = F)

Arguments

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
study	boolean value: defaults to F, determines whether or not to return acceptance probabilities

Value

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

Examples

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(N_schedule = rep(10000, 3), global_T = 0.5,
                                            start_beta = 1/4, base_samples = input_samples, study = T)

# plot results
plot_levels_seq_exp_4(test, from = -3, to = 3, plot_rows = 2, plot_columns = 2)

rchan26/exp4Tempering documentation built on June 20, 2019, 10:07 p.m.