def f2():
f1()
# for i in autoname(name="loop")(range(3)): <- this works too
for i in autoname(range(3), name="loop"):
f1()
sample("x", dist.Bernoulli(0.5))
return sample(dist.Normal(0.0, 1.0))
def geometric(p):
x = sample("x", dist.Bernoulli(p))
if x.item() == 1.0:
# model1()
return x + geometric(p)
else:
return x
def f2():
f1()
for i in range(3):
f1()
sample("x", dist.Bernoulli(0.5))
return sample(dist.Normal(0.0, 1.0))
def f1():
return sample(dist.Bernoulli(0.5))
def f3():
f1()
f1()
f1()
f2()
return sample(dist.Normal(0, 1))
def f2():
f1()
return sample(dist.Bernoulli(0.5))
def f2():
sample("x", dist.Bernoulli(0.5))
sample("x", dist.Bernoulli(0.5))
sample("x", dist.Bernoulli(0.5))
return sample(dist.Normal(0.0, 1.0))
def model():
a = pyro.param("a", torch.tensor(0.5))
return sample("b", dist.Bernoulli(a))
def f1():
sample(dist.Normal(0, 1))
sample(dist.Normal(0, 1))
return sample(dist.Bernoulli(0.5))
def model2(n):
sample("b", dist.Bernoulli(0.5))
if n <= 0:
return
else:
model1(n)
def model1():
with autoname(name="a"):
with autoname(name="b"):
sample("x", dist.Bernoulli(0.5))
def model2():
return sample("y", dist.Normal(0.0, 1.0))
def guide():
sample("x", dist.Gamma(1, 1))
return sample(dist.Bernoulli(0.5))
def model():
sample("x", dist.HalfNormal(1))
return sample(dist.Bernoulli(0.5))
def f2():
for i in autoname(pyro.plate(name="data", size=3)):
for j in autoname(range(2), name="xy"):
f1()
return sample(dist.Bernoulli(0.5))
