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Describe the bug I don't get a proper convergence with SNLE and SNRE for a multidimensional prior and simulatoe with noise.
To Reproduce
Versions Python version: 3.9.13 SBI version: 0.23.1
Minimal code example
scale_true = torch.tensor([[2.0, 1.5, 1.0, 2.0]]) # Define the prior num_dim = 4 num_sim = 1000 num_rounds = 2 initial_cond = torch.tensor([1.0, 1.0, 2.0,1.2]) n = 1000 t = np.linspace(0,10,n) # prior uni_prior_2D = BoxUniform(low=0* torch.ones(num_dim), high=3* torch.ones(num_dim)) # Simulators def simulator_exp(input, theta, num_sim, n_dim): x = np.zeros((n_dim, num_sim, len(input))) for i in range(n_dim): x[i] = (1/initial_cond[i])*torch.exp(-input/theta[:,i].reshape(-1,1)) + 0.5*torch.rand_like(theta[:,i]).reshape(-1,1) return torch.tensor(x).reshape(num_sim, n_dim, len(input)).float() x_o = simulator_exp(t, scale_true, 1, num_dim) x_o = x_o.reshape(1, num_dim*len(t)) inference = NLE(uni_prior_2D) proposal = uni_prior_2D for _ in range(num_rounds): theta = proposal.sample((num_sim,)) x = simulator_exp(t, theta, num_sim, num_dim) # Simulate data x = x.reshape(num_sim, num_dim*len(t)) x = np.clip(x, 0, 8) _ = inference.append_simulations(theta, x).train() posterior = inference.build_posterior(mcmc_method="slice_np_vectorized", mcmc_parameters={"num_chains": 5, "thin": 5}) proposal = posterior.set_default_x(x_o)
All else same for SNLE.
SNRE:
SNLE:
Expected behavior A sharper and proper convergence of the posterior
The text was updated successfully, but these errors were encountered:
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Describe the bug
I don't get a proper convergence with SNLE and SNRE for a multidimensional prior and simulatoe with noise.
To Reproduce
Versions
Python version: 3.9.13
SBI version: 0.23.1
Minimal code example
All else same for SNLE.
SNRE:
SNLE:
Expected behavior
A sharper and proper convergence of the posterior
The text was updated successfully, but these errors were encountered: