def test_beta_binomial_hmc():
num_samples = 1000
total = 10
counts = dist.Binomial(total, 0.3).sample()
concentration1 = torch.tensor(0.5)
concentration0 = torch.tensor(1.5)
prior = dist.Beta(concentration1, concentration0)
likelihood = dist.Beta(1 + counts, 1 + total - counts)
posterior = dist.Beta(concentration1 + counts,
concentration0 + total - counts)
def model():
prob = pyro.sample("prob", prior)
pyro.sample("counts", dist.Binomial(total, prob), obs=counts)
reparam_model = poutine.reparam(model,
{"prob": ConjugateReparam(likelihood)})
kernel = HMC(reparam_model)
samples = MCMC(kernel, num_samples, warmup_steps=0).run()
pred = Predictive(reparam_model, samples, num_samples=num_samples)
trace = pred.get_vectorized_trace()
samples = trace.nodes["prob"]["value"]
assert_close(samples.mean(), posterior.mean, atol=0.01)
assert_close(samples.std(), posterior.variance.sqrt(), atol=0.01)
def test_posterior_predictive_svi_auto_diag_normal_guide(return_trace):
true_probs = torch.ones(5) * 0.7
num_trials = torch.ones(5) * 1000
num_success = dist.Binomial(num_trials, true_probs).sample()
conditioned_model = poutine.condition(model, data={"obs": num_success})
guide = AutoDiagonalNormal(conditioned_model)
svi = SVI(conditioned_model, guide, optim.Adam(dict(lr=0.1)), Trace_ELBO())
for i in range(1000):
svi.step(num_trials)
posterior_predictive = Predictive(model, guide=guide, num_samples=10000, parallel=True)
if return_trace:
marginal_return_vals = posterior_predictive.get_vectorized_trace(num_trials).nodes["obs"]["value"]
else:
marginal_return_vals = posterior_predictive.get_samples(num_trials)["obs"]
assert_close(marginal_return_vals.mean(dim=0), torch.ones(5) * 700, rtol=0.05)