I’ve been working with a truncated BetaBinomial distribution, and while trying to simulate data (via pm.draw() or pm.sample_prior_predictive), I often encounter Truncation Errors (`TruncationError: Truncation did not converge in 10000 steps`

). From what I see in the documentation and the code behind `pm.Truncated`

, this error should only arise if rejection sampling is used, and it should only be used when the truncated distribution does not implement a `logcdf`

function. But `pm.BetaBinomial`

has `logcdf`

method, and so I am not sure why this is happening. Am I doing something wrong and are there ways to avoid / minimize the risk of such errors?

Sample code:

```
from pymc.distributions.mixture import _hurdle_mixture
class HurdleBetaBinomial:
def __new__(cls, name, psi, n, alpha, beta, **kwargs):
return _hurdle_mixture(
name=name, nonzero_p=psi, nonzero_dist=pm.BetaBinomial.dist(n=n, alpha=alpha, beta=beta), dtype="int", **kwargs
)
@classmethod
def dist(cls, psi, n, alpha, beta, **kwargs):
return _hurdle_mixture(
name=None, nonzero_p=psi, nonzero_dist=pm.BetaBinomial.dist(n=n, alpha=alpha, beta=beta), dtype="int", **kwargs
)
N = 1
with pm.Model() as m:
treatment = pm.Bernoulli("treatment", p=0.5, shape=N)
baseline = pm.Normal("inv_p", 0.2, 1, shape=N)
β = pm.Gamma("β", alpha=1, beta=4, shape=N)
α = pm.Gamma("α", alpha=1, beta=2, shape=N)
ψ_treatment = pm.Normal("inv_ψ_effect", 0, 0.2)
α_treatment = pm.Normal("α_effect", 0, 0.02)
β_treatment = pm.Normal("β_effect", 0, 0.02)
ψ = pm.Deterministic("ψ", pm.invlogit(baseline + ψ_treatment * treatment))
days = HurdleBetaBinomial(
"days",
n=7,
psi=ψ,
alpha=α + α_treatment * treatment,
beta=β + β_treatment * treatment,
shape=N, dims="tracks"
)
sim_model = pm.do(m, {
"inv_ψ_effect": 0,
"α_effect": 0.1,
"β_effect": 0
})
#try 10 times
for i in range(10):
with sim_model:
simulate = pm.sample_prior_predictive(samples=10_000)
```