diff --git a/matlab/default_option_values.m b/matlab/default_option_values.m index 9288951466ddf311b10dd224e80fe014cf7a1fa4..00748792f69c164c6ea7ffe31f2b6961c2da7393 100644 --- a/matlab/default_option_values.m +++ b/matlab/default_option_values.m @@ -515,7 +515,7 @@ options_.posterior_sampler_options.dsmh.tau = 10 ; options_.posterior_sampler_options.online.particles= 5000 ; options_.posterior_sampler_options.online.liu_west_delta = 0.99 ; options_.posterior_sampler_options.online.liu_west_max_resampling_tries = 5000 ; -options_.posterior_sampler_options.online.systematic_resampling = false; +options_.posterior_sampler_options.online.second_resampling = false; options_.trace_plot_ma = 200; options_.mh_autocorrelation_function_size = 30; diff --git a/matlab/estimation/check_posterior_sampler_options.m b/matlab/estimation/check_posterior_sampler_options.m index ccf43fed140479358fa53dcc5b49ad21091b2a9a..fe93f77a69ec5436c5ee84ed80b28f28c25e25e3 100644 --- a/matlab/estimation/check_posterior_sampler_options.m +++ b/matlab/estimation/check_posterior_sampler_options.m @@ -459,8 +459,8 @@ if init posterior_sampler_options.liu_west_delta = options_list{i,2}; case 'liu_west_max_resampling_tries' posterior_sampler_options.liu_west_max_resampling_tries = options_list{i,2}; - case 'systematic_resampling' - posterior_sampler_options.systematic_resampling = options_list{i,2}; + case 'second_resampling' + posterior_sampler_options.second_resampling = options_list{i,2}; otherwise warning(['online: Unknown option (' options_list{i,1} ')!']) end diff --git a/matlab/estimation/online/online_auxiliary_filter.m b/matlab/estimation/online/online_auxiliary_filter.m index fbd693638d218533c2baea19796e5d0dacfc47de..dc467f732062957969135df939914c415fa994c5 100644 --- a/matlab/estimation/online/online_auxiliary_filter.m +++ b/matlab/estimation/online/online_auxiliary_filter.m @@ -313,7 +313,7 @@ for t=1:sample_size variance_update = false; end % final resampling (not advised) - if online_opt.systematic_resampling + if online_opt.second_resampling indx = kitagawa(weights); StateVectors = StateVectors(:,indx) ; if pruning