def fun_extract_median_ess(sampler): # median ess # remove failed chains first sampler.remove_failed_chains() full_mcmc_tensor = get_params_mcmc_tensor(sampler=sampler) ess = ess_stan(full_mcmc_tensor) median_ess = numpy.median(ess) return ([median_ess], ["median_ess"])
print(samples[:,:,hidden_in_sigma2_indices]) #exit() #samples[:,:,hidden_in_sigma2_indices] = numpy.exp(samples[:,:,hidden_in_sigma2_indices]) posterior_mean_hidden_in_sigma2 = numpy.mean(samples[:,:,hidden_in_sigma2_indices].reshape(-1,len(hidden_in_sigma2_indices)),axis=0) posterior_median_hidden_in_sigma2 = numpy.median(samples[:,:,hidden_in_sigma2_indices].reshape(-1,len(hidden_in_sigma2_indices)),axis=0) print("diagnostics sigma2") print(diagnostics_stan(samples[:,:,hidden_in_sigma2_indices])) print("posterior mean sigma2 {}".format(posterior_mean_hidden_in_sigma2)) print("posterior median sigma2 {}".format(posterior_median_hidden_in_sigma2)) #print(mcmc_samples_beta["indices_dict"]) full_mcmc_tensor = get_params_mcmc_tensor(sampler=sampler1) print(get_short_diagnostics(full_mcmc_tensor)) out = sampler1.get_diagnostics(permuted=False) print("divergent") processed_diag = process_diagnostics(out,name_list=["divergent"]) print(processed_diag.sum(axis=1)) #print(processed_diag.shape) #processed_energy = process_diagnostics(out,name_list=["prop_H"])