def model(dim):
y = pyro.sample('y', dist.Normal(0, 3))
pyro.sample(
'x',
dist.TransformedDistribution(
dist.Normal(ops.zeros(dim - 1), 1),
dist.transforms.AffineTransform(0, ops.exp(y / 2))))
def model():
locs = pyro.param("locs",
ops.randn(3),
constraint=dist.constraints.real)
scales = pyro.param("scales",
ops.exp(ops.randn(3)),
constraint=dist.constraints.positive)
p = ops.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():
q = pyro.param("q",
ops.exp(ops.randn(3)),
constraint=dist.constraints.simplex)
pyro.sample("x", dist.Categorical(q))