def model():
a = yield pm.Normal("a", 0, 1)
b = yield pm.HalfNormal("b", 1)
c = yield pm.Normal("c",
loc=a,
scale=b,
event_stack=len(observed_value))
def model():
mu = yield pm.Normal("mu", tf.zeros(4), 1)
scale = yield pm.HalfNormal("scale", 1)
x = yield pm.Normal("x",
mu,
scale,
batch_stack=5,
observed=observed)
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 schools_pm4():
eta = yield pm4.Normal("eta", 0, 1, plate=J)
mu = yield pm4.Normal("mu", 1, 10)
tau = yield pm4.HalfNormal("tau", 1 * 2.0)
theta = mu + tau * eta
obs = yield pm4.Normal("obs", theta, sigma, observed=y)
return obs
def linreg(n_points=100):
# Define priors
sigma = pm.HalfNormal("sigma", scale=10)
intercept = pm.Normal("intercept", 0, scale=10)
x_coeff = pm.Normal("weight", 0, scale=5)
x = np.linspace(-5, 5, n_points)
# Define likelihood
y = pm.Normal("y", loc=intercept + x_coeff * x, scale=sigma)
def model():
mu = yield pm.Normal(
"mu",
tf.zeros(4),
1,
conditionally_independent=True,
reinterpreted_batch_ndims=1,
)
scale = yield pm.HalfNormal("scale",
1,
conditionally_independent=True)
x = yield pm.Normal(
"x",
mu,
scale[..., None],
observed=observed,
reinterpreted_batch_ndims=1,
event_stack=5,
)
def model():
beta = yield pm.Normal(
"beta",
tf.zeros((n_features, )),
1,
conditionally_independent=True,
reinterpreted_batch_ndims=1,
)
bias = yield pm.Normal("bias",
0,
1,
conditionally_independent=True)
scale = yield pm.HalfNormal("scale",
1,
conditionally_independent=True)
mu = tf.linalg.matvec(regressors, beta) + bias[..., None]
y = yield pm.Normal(
"y",
mu,
scale[..., None],
observed=observed,
reinterpreted_batch_ndims=1,
)
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 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
def model():
beta = yield pm.Normal("beta", tf.zeros((n_features, )), 1)
bias = yield pm.Normal("bias", 0, 1)
scale = yield pm.HalfNormal("scale", 1)
mu = tf.linalg.matvec(regressors, beta) + bias
y = yield pm.Normal("y", mu, scale, observed=observed)
def a_model():
try:
yield 1
except:
pass
yield pm.HalfNormal("n", 1, transform=pm.distributions.transforms.Log())
def a_model():
return (yield pm.HalfNormal("n", 1, transform=pm.distributions.transforms.Log()))
def invdalid_model():
yield pm.HalfNormal("n", 1, transform=pm.distributions.transforms.Log())
yield pm.Normal("n", 0, 1)