def model():
with pyro.plate_stack("plates", shape):
with pyro.plate("particles", 200000):
if "dist_type" == "Normal":
pyro.sample("x", dist.Normal(loc, scale))
else:
pyro.sample("x", dist.StudentT(10.0, loc, scale))
def model():
with pyro.plate_stack("plates", shape):
if "dist_type" == "Normal":
return pyro.sample("x", dist.Normal(loc, scale))
elif "dist_type" == "StudentT":
return pyro.sample("x", dist.StudentT(10.0, loc, scale))
else:
return pyro.sample("x",
dist.AsymmetricLaplace(loc, scale, 1.5))
def model():
fn = dist.TransformedDistribution(
dist.Normal(torch.zeros_like(loc), torch.ones_like(scale)),
[AffineTransform(loc, scale),
ExpTransform()])
if event_shape:
fn = fn.to_event(len(event_shape))
with pyro.plate_stack("plates", batch_shape):
with pyro.plate("particles", 200000):
return pyro.sample("x", fn)
def model():
with pyro.plate_stack("plates", shape):
with pyro.plate("particles", 200000):
return pyro.sample(
"x",
dist.TransformedDistribution(
dist.Normal(torch.zeros_like(loc),
torch.ones_like(scale)),
[AffineTransform(loc, scale),
ExpTransform()]))
def model(data):
hmm = dist.LinearHMM(init_dist,
trans_mat,
trans_dist,
obs_mat,
obs_dist,
duration=num_steps)
with pyro.plate_stack("plates", batch_shape):
z = pyro.sample("z", hmm)
pyro.sample("x", dist.Normal(z, 1).to_event(2), obs=data)
def model(self, zero_data, covariates):
with pyro.plate_stack("batch", zero_data.shape[:-2], rightmost_dim=-2):
loc = zero_data[..., :1, :]
scale = pyro.sample("scale", dist.LogNormal(loc, 1).to_event(1))
with self.time_plate:
jumps = pyro.sample("jumps", dist.Normal(0, scale).to_event(1))
prediction = jumps.cumsum(-2)
noise_dist = dist.Laplace(0, 1)
self.predict(noise_dist, prediction)
def model(l):
# Dimension -1 of `l` represents the number of rounds
# Other dimensions are batch dimensions: we indicate this with a plate_stack
with pyro.plate_stack("plate", l.shape[:-1]):
theta = pyro.sample("theta", dist.Normal(prior_mean, prior_sd))
# Share theta across the number of rounds of the experiment
# This represents repeatedly testing the same participant
theta = theta.unsqueeze(-1)
# This define a *logistic regression* model for y
logit_p = sensitivity * (theta - l)
# The event shape represents responses from the same participant
y = pyro.sample("y", dist.Bernoulli(logits=logit_p).to_event(1))
return y
def model(self, zero_data, covariates):
with pyro.plate_stack("batch", zero_data.shape[:-2], rightmost_dim=-2):
loc = zero_data[..., :1, :]
scale = pyro.sample("scale", dist.LogNormal(loc, 1).to_event(1))
with self.time_plate:
jumps = pyro.sample("jumps", dist.Normal(0, scale).to_event(1))
prediction = jumps.cumsum(-2)
duration, obs_dim = zero_data.shape[-2:]
noise_dist = dist.GaussianHMM(
dist.Normal(0, 1).expand([obs_dim]).to_event(1),
torch.eye(obs_dim),
dist.Normal(0, 1).expand([obs_dim]).to_event(1),
torch.eye(obs_dim),
dist.Normal(0, 1).expand([obs_dim]).to_event(1),
duration=duration,
)
self.predict(noise_dist, prediction)
def model(self, zero_data, covariates):
with pyro.plate_stack("batch", zero_data.shape[:-2], rightmost_dim=-2):
loc = zero_data[..., :1, :]
scale = pyro.sample("scale", dist.LogNormal(loc, 1).to_event(1))
with self.time_plate:
jumps = pyro.sample("jumps", dist.Normal(0, scale).to_event(1))
prediction = jumps.cumsum(-2)
duration, obs_dim = zero_data.shape[-2:]
noise_dist = dist.LinearHMM(
dist.Stable(1.9, 0).expand([obs_dim]).to_event(1),
torch.eye(obs_dim),
dist.Stable(1.9, 0).expand([obs_dim]).to_event(1),
torch.eye(obs_dim),
dist.Stable(1.9, 0).expand([obs_dim]).to_event(1),
duration=duration,
)
rep = StableReparam()
with poutine.reparam(
config={"residual": LinearHMMReparam(rep, rep, rep)}):
self.predict(noise_dist, prediction)
def model():
with pyro.plate_stack("plates", shape):
with pyro.plate("particles", 200000):
return pyro.sample("x", dist.Stable(stability, 0, scale, loc))
def model():
with pyro.plate_stack("plates", shape[:dim]):
return pyro.sample("x", dist.Normal(loc, scale).to_event(-dim))
def model(data=None):
with pyro.plate_stack("plates", batch_shape):
return pyro.sample("x", hmm, obs=data)
def model():
with pyro.plate_stack("plates", shape):
with pyro.plate("particles", 10000):
pyro.sample(
"x",
dist.RelaxedOneHotCategorical(temperature, logits=logits))
def model():
with pyro.plate_stack("plates", batch_shape):
return pyro.sample("x", dist.Normal(loc, scale).to_event(len(event_shape)))
def guide(data):
with pyro.plate_stack("plates", batch_shape):
pyro.sample("z", posterior)
def model():
with pyro.plate_stack("plates", batch_shape):
return pyro.sample("x", hmm)
def model():
with pyro.plate_stack("plates", shape):
return pyro.sample("x", dist.StudentT(df, loc, scale))
def guide():
loc = pyro.param("loc", torch.tensor(0.))
with pyro.plate_stack("plates", (2,) * depth):
return pyro.sample("x", dist.Normal(loc, 1).has_rsample_(has_rsample))
def model():
with pyro.plate_stack("plates", shape[:dim]):
with pyro.plate("particles", 10000):
pyro.sample("x",
dist.Uniform(0, 1).expand(shape).to_event(-dim))
def model():
with pyro.plate_stack("plates", shape[:dim]):
with pyro.plate("particles", 10000):
pyro.sample(
"x",
dist.Normal(loc, scale).expand(shape).to_event(-dim))
def model():
with pyro.plate_stack("plates", shape):
return pyro.sample("x", dist.ProjectedNormal(concentration))
def model(data):
with pyro.plate_stack("plates", batch_shape):
z = pyro.sample("z", prior)
pyro.sample("x", dist.Normal(z, 1).to_event(2), obs=data)
def plate(self) -> pyro.plate:
return (self.plate_stack if isinstance(
self.plate_stack, tp.ContextManager) else pyro.plate_stack(
'plate', self.plate_stack))
def model():
with pyro.plate_stack("plates", shape):
with pyro.plate("particles", 10000):
pyro.sample("x", dist.ProjectedNormal(concentration))