kde=False)
# Prior push-forward
ref_p_prior_pf = Distribution(ref_prior_pf_samples,
rv_name='$Q$',
label='Prior-PF')
ref_p_prior_pf.eval_kernel_density()
# Observed density
obs_loc = [0.25]
obs_scale = [0.1]
obs_samples = np.random.randn(n_mc_ref,
len(obs_scale)) * obs_scale + obs_loc
obs_samples = np.reshape(obs_samples, (n_mc_ref, np.shape(obs_samples)[1]))
p_obs = Distribution(obs_samples, rv_name='$Q$', label='Observed')
p_obs_evals = p_obs.kernel_density(ref_prior_pf_samples.T)
# Reference r
ref_r = p_obs_evals / (
ref_p_prior_pf.kernel_density(ref_prior_pf_samples.T) + 1.0e-10)
l1_posterior_1hf_avg = np.zeros((n_grid, ))
l1_posterior_1hf_1lf_avg = np.zeros((n_grid, ))
l1_posterior_1hf_2lf_avg = np.zeros((n_grid, ))
for k in range(n_avg):
print('\nRun %d / %d' % (k + 1, n_avg))
# -------------- 1 HF
l1_posterior_1hf = []
for idx, n_evals in enumerate(n_evals_mc):
