def constrained_model(data):
locs = pyro.param("locs", torch.randn(3), constraint=constraints.real)
scales = pyro.param("scales",
ops.exp(torch.randn(3)),
constraint=constraints.positive)
p = torch.tensor([0.5, 0.3, 0.2])
x = pyro.sample("x", dist.Categorical(p))
pyro.sample("obs", dist.Normal(locs[x], scales[x]), obs=data)
def guide(data):
guide_loc = pyro.param("guide_loc", torch.tensor(0.))
guide_scale = ops.exp(pyro.param("guide_scale_log", torch.tensor(0.)))
pyro.sample("loc", dist.Normal(guide_loc, guide_scale))
def guide_constrained_model(data):
q = pyro.param("q",
ops.exp(torch.randn(3)),
constraint=constraints.simplex)
pyro.sample("x", dist.Categorical(q))