def model():
p = torch.tensor(0.5)
for i in pyro.irange("irange", 2):
# Each loop iteration should give the sample site a different name.
pyro.sample("x", dist.Bernoulli(p))
def model():
with pyro.iarange("particles", num_particles):
pyro.sample("x1", dist.Bernoulli(0.1).expand_by([num_particles]))
pyro.sample("x2", dist.Bernoulli(0.2).expand_by([num_particles]))
pyro.sample("x3", dist.Bernoulli(0.3).expand_by([num_particles]))
def model():
with pyro.iarange("particles", num_particles):
pyro.sample("y", dist.Bernoulli(p).expand_by([num_particles]))
with pyro.iarange("iarange", iarange_dim):
pyro.sample("z", dist.Bernoulli(p).expand_by([iarange_dim, num_particles]))
def model():
scale = pyro.param("scale")
loc = pyro.sample("loc", dist.Normal(0., 10.))
with pyro.iarange("data", len(data)):
pyro.sample("obs", dist.Normal(loc, scale), obs=data)
pyro.sample("b", dist.Bernoulli(0.5))
def guide():
q = pyro.param("q")
with pyro.iarange("particles", num_particles):
pyro.sample("z", dist.Bernoulli(q).expand_by([num_particles]))
def guide():
p = pyro.param("p", torch.tensor(0.5, requires_grad=True))
x = pyro.sample("x", dist.Bernoulli(p))
assert x.shape == torch.Size([2]) + iarange_shape
def guide():
p = pyro.param("p", torch.tensor(0.5, requires_grad=True))
pyro.sample("x", dist.Bernoulli(p))
def model():
p = torch.tensor(0.5)
with pyro.iarange("iarange", 3, 2) as ind:
for i in pyro.irange("irange", 3, 2):
pyro.sample("x_{}".format(i),
dist.Bernoulli(p).expand_by([len(ind)]))
def guide():
p = pyro.param("p", torch.tensor(0.5, requires_grad=True))
with pyro.iarange("iarange", 3, 2) as ind:
for i in pyro.irange("irange", 3, 2):
pyro.sample("x_{}".format(i),
dist.Bernoulli(p).expand_by([len(ind)]))
def guide():
p = pyro.param("p", torch.tensor(0.5, requires_grad=True))
for i in pyro.irange("irange", 10, subsample_size):
pass
pyro.sample("x", dist.Bernoulli(p))
def model():
p = torch.tensor(0.5, requires_grad=True)
with pyro.iarange("iarange", 10, 5):
pyro.sample("x", dist.Bernoulli(p).expand_by([1]))
def model():
p = torch.tensor(0.5)
for i in pyro.irange("irange", 10, subsample_size):
pass
pyro.sample("x", dist.Bernoulli(p))
def model():
p = torch.tensor(0.5)
with pyro.iarange("iarange"):
pyro.sample("x", dist.Bernoulli(p).expand_by([10]))
def guide():
p = pyro.param("p", torch.tensor(0.5, requires_grad=True))
for i in pyro.irange("irange", 2):
# Each loop iteration should give the sample site a different name.
pyro.sample("x", dist.Bernoulli(p))
def model():
p = torch.tensor(0.5)
pyro.sample("x", dist.Bernoulli(p).expand_by([5]))
def model():
p = torch.tensor(0.5, requires_grad=True)
with pyro.iarange("iarange_0", 10, 5) as ind1:
pyro.sample("x0", dist.Bernoulli(p).expand_by([len(ind1)]))
with pyro.iarange("iarange_1", 11, 6) as ind2:
pyro.sample("x1", dist.Bernoulli(p).expand_by([len(ind2)]))
def model():
p = torch.tensor(0.5)
x = pyro.sample("x", dist.Bernoulli(p))
assert x.shape == torch.Size([2]) + iarange_shape
def guide():
p = pyro.param("p", torch.tensor(0.5, requires_grad=True))
pyro.sample("x", dist.Bernoulli(p), infer={"enumerate": enumerate_})
def model():
p = torch.tensor(0.5)
pyro.sample("x", dist.Bernoulli(p))
pyro.sample("x", dist.Bernoulli(p)) # Should error here.
def model():
p = torch.tensor(0.5)
pyro.sample("x", dist.Bernoulli(p))
pyro.sample("y", dist.Bernoulli(p))
def model(num_particles):
p = pyro.param("p")
with pyro.plate("num_particles", num_particles, dim=-2):
z = pyro.sample("z", dist.Bernoulli(p))
with pyro.plate("data", 3):
pyro.sample("x", dist.Normal(z, 1.), obs=data)
def model():
p = torch.tensor(0.5)
for i in pyro.irange("irange", 10, 5):
pyro.sample("x_{}".format(i), dist.Bernoulli(p))
def model():
with pyro.iarange("particles", num_particles):
pyro.sample("z", dist.Bernoulli(0.25).expand_by([num_particles]))
def guide():
p = pyro.param("p", torch.tensor(0.5, requires_grad=True))
for i in pyro.irange("irange", 10, 5):
pyro.sample("x_{}".format(i), dist.Bernoulli(p))
def model(depth):
for i in range(depth):
pyro.sample("x{}".format(i), dist.Bernoulli(0.5))
def model():
p = torch.tensor(0.5)
with pyro.iarange("iarange", 10, 5) as ind:
pyro.sample("x", dist.Bernoulli(p).expand_by([len(ind)]))
def guide():
q = pyro.param("q")
with pyro.iarange("particles", num_particles):
pyro.sample("x1", dist.Bernoulli(q).expand_by([num_particles]), infer={"enumerate": enumerate1})
pyro.sample("x2", dist.Bernoulli(q).expand_by([num_particles]), infer={"enumerate": enumerate2})
pyro.sample("x3", dist.Bernoulli(q).expand_by([num_particles]), infer={"enumerate": enumerate3})
def guide():
p = pyro.param("p", torch.tensor(0.5, requires_grad=True))
with pyro.iarange("iarange", 10, 5) as ind:
pyro.sample("x", dist.Bernoulli(p).expand_by([len(ind)]))
def model():
with pyro.iarange("particles", num_particles):
pyro.sample("x", dist.Bernoulli(p).expand_by([num_particles]))
for i in pyro.irange("irange", irange_dim):
pyro.sample("y_{}".format(i), dist.Bernoulli(p).expand_by([num_particles]))
def model():
p = pyro.sample("p", dist.Beta(2., 2.))
x = pyro.sample("x", dist.Bernoulli(p))
pyro.sample("obs",
p * x + (1 - p) * (1 - x),
obs=torch.tensor([1., 0.]))
