def model(y):
lambda1 = numpyro.sample(
'lambda1', dist.ImproperUniform(dist.constraints.real, (), ()))
lambda2 = numpyro.sample(
'lambda2', dist.ImproperUniform(dist.constraints.real, (), ()))
sigma = numpyro.sample(
'sigma', dist.ImproperUniform(dist.constraints.positive, (), ()))
mu = numpyro.deterministic('mu', lambda1 + lambda2)
numpyro.sample('y', dist.Normal(mu, sigma), obs=y)
def model(y):
lambda1 = numpyro.sample(
"lambda1", dist.ImproperUniform(dist.constraints.real, (), ()))
lambda2 = numpyro.sample(
"lambda2", dist.ImproperUniform(dist.constraints.real, (), ()))
sigma = numpyro.sample(
"sigma", dist.ImproperUniform(dist.constraints.positive, (), ()))
mu = numpyro.deterministic("mu", lambda1 + lambda2)
numpyro.sample("y", dist.Normal(mu, sigma), obs=y)
def model():
population = jnp.array([1000., 2000., 3000.])
with numpyro.plate("region", 3):
d = dist.ImproperUniform(support=constraints.interval(0, population),
batch_shape=(3,),
event_shape=event_shape)
incidence = numpyro.sample("incidence", d)
assert d.log_prob(incidence).shape == (3,)
def __call__(self, name, fn, obs):
# Support must be circular
support = fn.support
if isinstance(support, constraints.independent):
support = fn.support.base_constraint
assert support is constraints.circular
# Draw parameter-free noise.
new_fn = dist.ImproperUniform(constraints.real, fn.batch_shape, fn.event_shape)
value = numpyro.sample(
f"{name}_unwrapped",
new_fn,
obs=obs,
)
# Differentiably transform.
value = jnp.remainder(value + math.pi, 2 * math.pi) - math.pi
# Simulate a pyro.deterministic() site.
numpyro.factor(f"{name}_factor", fn.log_prob(value))
return None, value
def model():
x = numpyro.sample(
"x", dist.ImproperUniform(dist.constraints.positive, (), ()))
return numpyro.sample("y", dist.Normal(x, 1), obs=1.0)
def model(data):
mean = numpyro.sample("mean", dist.Normal(0, 1).mask(False))
std = numpyro.sample(
"std", dist.ImproperUniform(dist.constraints.positive, (), ()))
return numpyro.sample("obs", dist.Normal(mean, std), obs=data)
def model():
numpyro.sample("c", dist.Bernoulli(0.8))
numpyro.sample(
"u", dist.ImproperUniform(dist.constraints.unit_interval, (), ())
)
