Hi there `:)`

As the title says, I’m looking for a way to build a model where the variables are in same ranges with different values, however the result sampling suggest the sampling points are away from the switch constraints.

So, what do you think about this?

```
with pm.Model() as model:
rs = pm.Normal('rs', 800, 50)
rp = pm.Normal('rp', 800, 50)
re = pm.Normal('re', 800, 50)
pm.Potential('const1', tt.switch(tt.gt(rp, rs), 0., -1E10))
pm.Potential('const2', tt.switch(tt.gt(re, rp), 0., -1E10))
rhop = pm.Uniform('rhop', 1, 5)
rhoe = pm.Uniform('rhoe', 1, 5)
pm.Potential('const3', tt.switch(tt.gt(rhop, rhoe), 0., -1E10))
aa = pm.Deterministic('aa', np.sqrt(-np.log(rhoe / rhop)))
trace = pm.sample(1000)
data = az.from_pymc3(trace)
pm.summary(data, hdi_prob=0.95)
```

```
mean sd hdi_2.5% hdi_97.5% ... mcse_sd ess_bulk ess_tail r_hat
rs 793.549 48.173 698.735 888.375 ... 9.795 13.0 283.0 1.13
rp 803.784 49.538 710.618 904.680 ... 2.813 157.0 294.0 1.02
re 805.033 41.822 727.193 890.052 ... 2.876 105.0 188.0 1.00
rhop 2.821 1.165 1.000 4.724 ... 0.154 32.0 126.0 1.06
rhoe 3.064 1.203 1.165 4.995 ... 0.140 38.0 109.0 1.05
aa NaN NaN 0.027 NaN ... NaN NaN NaN NaN
```