def model(N, D1, D2, data=None):
with pyro.plate("plate_0", D1):
alpha = pyro.sample("alpha", dist.HalfCauchy(1.))
beta = pyro.sample("beta", dist.HalfCauchy(1.))
with pyro.plate("plate_1", D2):
probs = pyro.sample("probs", dist.Beta(alpha, beta))
with pyro.plate("data", N):
pyro.sample("binomial",
dist.Binomial(probs=probs,
total_count=total_count),
obs=data)
def model(J, sigma, y=None):
mu = pyro.sample('mu', dist.Normal(0, 5))
tau = pyro.sample('tau', dist.HalfCauchy(5))
with pyro.plate('J', J):
theta = pyro.sample('theta', dist.Normal(mu, tau))
pyro.sample('obs', dist.Normal(theta, sigma), obs=y)
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(data=None):
loc = pyro.param("loc", ops.tensor(2.0))
scale = pyro.param("scale", ops.tensor(1.0))
with pyro.plate("data", 1000, dim=-1):
x = pyro.sample("x", dist.Normal(loc, scale), obs=data)
return x
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():
loc = pyro.param("loc", ops.tensor(0.))
scale = pyro.param("scale", ops.tensor(1.))
with pyro.plate("plate_outer", data.shape[-1], dim=-1):
pyro.sample("x", dist.Normal(loc, scale))
def model():
loc = ops.tensor(3.0)
with pyro.plate("plate_outer", data.shape[-1], dim=-1):
x = pyro.sample("x", dist.Normal(loc, 1.))
with pyro.plate("plate_inner", data.shape[-2], dim=-2):
pyro.sample("y", dist.Normal(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)