There were *divergences after tuning. Increase `target_accept` or reparameterize. & The number of effective samples is smaller than 10% for some parameters

[HG1]

Hi
It worked

with pm.Model() as model:
    alpha = pm.Uniform('alpha', lower=0, upper=50)
    beta =  pm.Uniform('beta', lower=0, upper=50)
    z = pm.BetaBinomial('z',n=6, alpha=alpha, beta=beta,observed=dist -1, shape=(413))

    idata = pm.sample(27000, tune=1500,init='adapt_diag', chains=4, cores=4)

It still gives the acceptance message however.
But now it is too high??

Auto-assigning NUTS sampler...
Initializing NUTS using adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [beta, alpha]
Sampling 4 chains, 0 divergences: 100%|██████████| 114000/114000 [00:48<00:00, 2361.94draws/s]
The acceptance probability does not match the target. It is 0.8982175303601605, but should be close to 0.8. Try to increase the number of tuning steps.
The acceptance probability does not match the target. It is 0.9146879438754264, but should be close to 0.8. Try to increase the number of tuning steps.
The number of effective samples is smaller than 25% for some parameters.

Super!
Thanks!