# Simple generative model, but divergent

Hi all,
A little way in to my pymc3 journey but having trouble with, what seems to be a simple model. Thinking generatively, I can construct test data which replicates the problem (i.e., it’s me not the data!). What’s strange is the prior predictive checks look good, but I have lots of divergences (even after reducing step size, and giving good testval hints). I’ve already centered the problem data. I guess I am in the funnel of doom?

Appreciate any help Generate test data:

``````    n_samples = 1000
# Goals of the inference are W, x, y
# The observed data is A1 to A4
W = np.random.normal(4, 0.05, n_samples)
# A random splitting
x = np.random.normal(0.5, 0.01, n_samples)
# Derived quantities - Level 1
B1 = W * (1-x)
B2 = W * x
# Two further random splittings
y = np.random.normal(0.5, 0.01, (n_samples, 2))
# Now generate what will be the observations
A1 = B1 * (1-y[:,0])
A2 = B1 * y[:,0]
A3 = B2 * (1-y[:,1])
A4 = B2 * y[:,1]
# and collect into a dataframe
df_test = pd.DataFrame({
'A1': A1,
'A2': A2,
'A3': A3,
'A4': A4})
``````

The model:

``````    with pm.Model() as test_model:
W = pm.Normal("W", 4.0, 1.0, testval=4.0)
x = pm.Normal("x", 0.05, 0.005, testval=0.05)

σ_B = pm.HalfNormal("σ_B",0.01, shape=2, testval=0.05)
B1 = pm.Normal("B1", W*x, σ_B, testval=2.0)
B2 = pm.Normal("B2", W*(1-x), σ_B, testval=2.0)

y = pm.Normal("y", 0.5, 0.01, shape=2, testval=0.5)
σ_a = pm.HalfNormal("σ_a", 0.05, shape=4, testval = 0.05)
A1 = pm.Normal("A1", B1*y, σ_a, observed=df_test["A1"].values)
A2 = pm.Normal("A2", B1*(1-y), σ_a, observed=df_test["A2"].values)
A3 = pm.Normal("A3", B2*y, σ_a, observed=df_test["A3"].values)
A4 = pm.Normal("A4", B2*(1-y), σ_a, observed=df_test["A4"].values)
``````

Running:

``````    with test_model:
# Only use one core to avoid Windows pipe error which will crash the kernel
test_trace = pm.sample(1000, tune=1000, cores=1, target_accept=.95)
``````