def model(dim):
y = pyro.sample('y', dist.Normal(0, 3))
pyro.sample(
'x',
dist.TransformedDistribution(
dist.Normal(ops.zeros(dim - 1), 1),
dist.transforms.AffineTransform(0, ops.exp(y / 2))))
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 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 beta_binomial():
N, D1, D2 = 10, 2, 2
true_probs = ops.tensor([[0.7, 0.4], [0.6, 0.4]])
total_count = ops.tensor([[1000, 600], [400, 800]])
data = pyro.sample('data',
dist.Binomial(total_count=total_count,
probs=true_probs),
sample_shape=(N, ))
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)
return {
'model': model,
'model_args': (N, D1, D2),
'model_kwargs': {
'data': data
}
}
def logistic_regression():
N, dim = 3000, 3
# generic way to sample from distributions
data = pyro.sample('data', dist.Normal(0., 1.), sample_shape=(N, dim))
true_coefs = ops.arange(1., dim + 1.)
logits = ops.sum(true_coefs * data, axis=-1)
labels = pyro.sample('labels', dist.Bernoulli(logits=logits))
def model(x, y=None):
coefs = pyro.sample('coefs', dist.Normal(ops.zeros(dim),
ops.ones(dim)))
intercept = pyro.sample('intercept', dist.Normal(0., 1.))
logits = ops.sum(coefs * x, axis=-1) + intercept
return pyro.sample('obs', dist.Bernoulli(logits=logits), obs=y)
return {
'model': model,
'model_args': (data, ),
'model_kwargs': {
'y': labels
}
}
def model(data):
loc = pyro.param("loc", ops.tensor(0.0))
pyro.sample("x", dist.Normal(loc, 1.), obs=data)
def model(data):
p = pyro.param("p", ops.tensor(0.5))
pyro.sample("x", dist.Bernoulli(p), obs=data)
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(data=None):
loc = pyro.param("loc", ops.tensor(2.0))
scale = pyro.param("scale", ops.tensor(1.0))
x = pyro.sample("x", dist.Normal(loc, scale), obs=data)
return x
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)
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():
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(x, y=None):
coefs = pyro.sample('coefs', dist.Normal(ops.zeros(dim),
ops.ones(dim)))
intercept = pyro.sample('intercept', dist.Normal(0., 1.))
logits = ops.sum(coefs * x, axis=-1) + intercept
return pyro.sample('obs', dist.Bernoulli(logits=logits), obs=y)
def guide():
q = pyro.param("q",
ops.exp(ops.randn(3)),
constraint=dist.constraints.simplex)
pyro.sample("x", dist.Categorical(q))
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 model():
x = pyro.sample("x", dist.Normal(0., 1.))
pyro.sample("y", dist.Normal(x, 1.))
def guide():
loc = pyro.param("loc", ops.tensor(0.))
x = pyro.sample("x", dist.Normal(loc, 1.))
pyro.sample("y", dist.Normal(x, 1.))
def model():
pyro.sample("x", dist.Normal(0, 1))
