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))
def guide():
with pyro.plate("plate", len(data), dim=-1):
p = pyro.param("p", ops.ones(len(data), 3) / 3, event_dim=1)
pyro.sample("x", dist.Categorical(p))
return p
def model():
locs = pyro.param("locs", ops.tensor([-1., 0., 1.]))
with pyro.plate("plate", len(data), dim=-1):
x = pyro.sample("x", dist.Categorical(ops.ones(3) / 3))
pyro.sample("obs", dist.Normal(locs[x], 1.), obs=data)
def guide():
p = pyro.param("p", ops.tensor([0.5, 0.3, 0.2]))
with pyro.plate("plate", len(data), dim=-1):
pyro.sample("x", dist.Categorical(p))
def model():
locs = pyro.param("locs", ops.tensor([0.2, 0.3, 0.5]))
p = ops.tensor([0.2, 0.3, 0.5])
with pyro.plate("plate", len(data), dim=-1):
x = pyro.sample("x", dist.Categorical(p))
pyro.sample("obs", dist.Normal(locs[x], 1.), obs=data)