def model():
sd = yield pm.Exponential("sd", 1)
x = yield pm.Normal("x", 0, 1, observed=observed_kwargs["model/x"])
y = yield pm.HalfNormal("y", 1, observed=observed_kwargs["model/y"])
d = yield pm.Deterministic("d", x + y)
z = yield pm.Normal("z", d, sd, observed=observed_kwargs["model/z"])
u = yield pm.Exponential("u", z)
return u
def outer_model():
cond = yield pm.HalfNormal("cond", 1)
dcond = yield pm.Deterministic("dcond", cond * 2)
dx = yield nested_model(dcond)
ddx = yield pm.Deterministic("ddx", dx)
return ddx
def nested_model(cond):
x = yield pm.Normal("x", cond, 1)
dx = yield pm.Deterministic("dx", x + 1)
return dx
def unvectorized_model():
norm = yield pm.Normal("norm", 0, 1, batch_stack=norm_shape)
determ = yield pm.Deterministic("determ", tf.reduce_max(norm))
output = yield pm.Normal("output", determ, 1, observed=observed)
def simple_model_with_deterministic():
norm = yield simple_model()
determ = yield pm.Deterministic("determ", norm * 2)
return determ
def model():
x = yield pm.Normal("x", 0, 1)
det = yield pm.Deterministic("det", x)
y = yield pm.Normal("det", det, 1)
return y
def model():
x = yield pm.Normal("x", 0, 1)
det = yield pm.Deterministic("x", x)
return det
def model():
x = yield pm.Normal("x", 0, 1)
yield pm.Deterministic(None, x)
def model():
sd = yield pm.HalfNormal("sd", 1.0)
mu = yield pm.Deterministic("mu", tf.convert_to_tensor(1.0))
x = yield pm.Normal("x", mu, sd, observed=observed)
y = yield pm.Normal("y", x, 1e-9)
dy = yield pm.Deterministic("dy", 2 * y)
def outer_model():
cond = yield pm.HalfNormal("cond", 1, conditionally_independent=True)
dcond = yield pm.Deterministic("dcond", cond * 2)
dx = yield nested_model(dcond)
ddx = yield pm.Deterministic("ddx", dx)
return ddx
