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 __init__(self, alpha, scale=1., validate_args=None):
batch_shape = lax.broadcast_shapes(np.shape(scale), np.shape(alpha))
self.scale, self.alpha = np.broadcast_to(scale,
batch_shape), np.broadcast_to(
alpha, batch_shape)
base_dist = Exponential(self.alpha)
transforms = [ExpTransform(), AffineTransform(loc=0, scale=self.scale)]
super(Pareto, self).__init__(base_dist,
transforms,
validate_args=validate_args)
def __init__(self, loc=0., scale=1., validate_args=None):
base_dist = Normal(loc, scale)
self.loc, self.scale = base_dist.loc, base_dist.scale
super(LogNormal, self).__init__(base_dist,
ExpTransform(),
validate_args=validate_args)
