Hi,
I’ve a simple model that fails in a situation where the measured data does not change. Here is the code:
import bambi as bmb
import pymc as pm
import pandas as pd
df = pd.DataFrame({
"x": [739159, 739190, 739220, 739251],
"y": [43, 43, 43, 43],
})
# Create the glm using the Bambi model syntax
model = bmb.Model("y ~ x", df, family="t")
model.set_priors({"nu": bmb.Prior("Gamma", alpha=3, beta=1)})
# Fit the model using a NUTS (No-U-Turn Sampler)
trace = model.fit(
draws=10000,
tune=1000,
discard_tuned_samples=True,
chains=4,
progressbar=True)
The error I get is
SamplingError: Initial evaluation of model at starting point failed!
Starting values:
{'sigma_log__': array(-inf), 'nu_log__': array(0.5193919), 'Intercept': array(43.14749544), 'x': array(0.48053908)}
Logp initial evaluation results:
{'sigma': -inf, 'nu': -0.82, 'Intercept': -inf, 'x': -inf, 'y': -inf}
You can call `model.debug()` for more details.
What do I need to change to make it more robust?
Best,
Thorsten