Hello !

While trying to implement a specific model, I saw that the Metropolis step seemed to never move.

I sneeked in pymc3 source code to add some prints and saw that the delta_logp was always very small.

I’m working with a black-box likelihood with `pm.Potential`

and when printing my log-likelihoods, I shouldn’t have such low `delta_logp`

, in my understanding…

Here is a quick output exemple :

```
Sequential sampling (2 chains in 1 job)
Metropolis: [law]
Sampling chain 0, 0 divergences: 0%| | 2/1100 [00:00<03:10, 5.77it/s]
returned log-likelihood: [[array(-29.67120048)]]
returned log-likelihood: [[array(-29.65408013)]]
delta_logp: -6.347108208972685
Accepted ? False
returned log-likelihood: [[array(-29.75667088)]]
returned log-likelihood: [[array(-29.65408013)]]
delta_logp: -9.220555196247574
Accepted ? False
Sampling chain 0, 0 divergences: 0%| | 4/1100 [00:00<02:37, 6.94it/s]
returned log-likelihood: [[array(-29.97665752)]]
returned log-likelihood: [[array(-29.65408013)]]
delta_logp: -10.00953635258783
Accepted ? False
returned log-likelihood: [[array(-29.70574403)]]
returned log-likelihood: [[array(-29.65408013)]]
delta_logp: -8.004581846022162
Accepted ? False
Sampling chain 0, 0 divergences: 1%| | 6/1100 [00:00<02:20, 7.76it/s]
returned log-likelihood: [[array(-29.62658475)]]
returned log-likelihood: [[array(-29.65408013)]]
delta_logp: -9.647356751319833
Accepted ? False
```

Do these numbers seem fine to you ? If so could you please explain to me how such small log-likelihood differences can create these `delta_logp`

?

Thanks a lot in advance.