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 model(data):
loc = pyro.sample("loc", dist.Normal(0., 1.))
with pyro.plate("data", len(data), dim=-1):
pyro.sample("obs", dist.Normal(loc, 1.), obs=data)
def model(data=None):
loc = pyro.param("loc", torch.tensor(expected_mean))
scale = pyro.param("scale", torch.tensor(1.0))
with pyro.plate("data", 1000, dim=-1):
x = pyro.sample("x", dist.Normal(loc, scale), obs=data)
return x
def model(data=None):
loc = pyro.param("loc", torch.tensor(2.0))
scale = pyro.param("scale", torch.tensor(1.0))
x = pyro.sample("x", dist.Normal(loc, scale), obs=data)
return x
def guide():
loc = pyro.param("loc", torch.tensor(0.0))
scale = pyro.param("scale", torch.tensor(1.0))
with pyro.plate("plate_outer", data.size(-1), dim=-1):
pyro.sample("x", dist.Normal(loc, scale))
def model():
locs = pyro.param("locs", torch.tensor([-1.0, 0.0, 1.0]))
with pyro.plate("plate", len(data), dim=-1):
x = pyro.sample("x", dist.Categorical(torch.ones(3) / 3))
pyro.sample("obs", dist.Normal(locs[x], 1.0), obs=data)
def model():
locs = pyro.param("locs", torch.tensor([0.2, 0.3, 0.5]))
p = torch.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], 1.0), obs=data)
def model():
loc = torch.tensor(3.0)
with pyro.plate("plate_outer", data.size(-1), dim=-1):
x = pyro.sample("x", dist.Normal(loc, 1.0))
with pyro.plate("plate_inner", data.size(-2), dim=-2):
pyro.sample("y", dist.Normal(x, 1.0), obs=data)
def model(data):
loc = pyro.param("loc", torch.tensor(0.0))
pyro.sample("x", dist.Normal(loc, 1.0), obs=data)
def guide(data):
guide_loc = pyro.param("guide_loc", torch.tensor(0.))
guide_scale = pyro.param("guide_scale",
torch.tensor(1.),
constraint=constraints.positive)
pyro.sample("loc", dist.Normal(guide_loc, guide_scale))
def guide():
loc = pyro.param("loc", torch.tensor(0.))
y = pyro.sample("y", dist.Normal(loc, 1.))
pyro.sample("x", dist.Normal(y, 1.))
def model():
x = pyro.sample("x", dist.Normal(0., 1.))
pyro.sample("y", dist.Normal(x, 1.))
def guide(data):
guide_loc = pyro.param("guide_loc", torch.tensor(0.))
guide_scale = pyro.param(
"guide_scale_log", torch.tensor(0.),
torch.distributions.constraints.positive).exp()
pyro.sample("loc", dist.Normal(guide_loc, guide_scale))