def model(loc, scale):
with numpyro.plate_stack("plates", shape[:len(shape) - event_dim]):
with numpyro.plate("particles", 10000):
if "dist_type" == "Normal":
numpyro.sample("x", dist.Normal(loc, scale).to_event(event_dim))
else:
numpyro.sample("x", dist.StudentT(10.0, loc, scale).to_event(event_dim))
def model():
with numpyro.plate_stack("plates", shape):
with numpyro.plate("particles", 100000):
return numpyro.sample(
"x",
dist.TransformedDistribution(
dist.Normal(jnp.zeros_like(loc), jnp.ones_like(scale)),
[AffineTransform(loc, scale),
ExpTransform()]).expand_by([100000]))
def model():
fn = dist.TransformedDistribution(
dist.Normal(jnp.zeros_like(loc), jnp.ones_like(scale)),
[AffineTransform(loc, scale), ExpTransform()]).expand(shape)
if event_shape:
fn = fn.to_event(len(event_shape)).expand_by([100000])
with numpyro.plate_stack("plates", batch_shape):
with numpyro.plate("particles", 100000):
return numpyro.sample("x", fn)
def guide():
with numpyro.plate_stack("plates", shape):
return numpyro.sample("x", dist.Normal(0, 1))
def model_exp(loc, concentration):
with numpyro.plate_stack("plates", shape):
with numpyro.plate("particles", 10000):
numpyro.sample("x", dist.VonMises(loc, concentration))
def model(concentration):
with numpyro.plate_stack("plates", shape):
with numpyro.plate("particles", 10000):
numpyro.sample("x", dist.ProjectedNormal(concentration))
