Is it possible to restrict the value of a Deterministic variable? For example, my model looks like this:

```
with pm.Model() as myModel:
sig = pm.HalfNormal('sig', sd=2)
kappa = pm.Normal('kappa', mu=1, sd=1)
rhoCp = pm.Normal('rhoCp', mu=4, sd=1)
alpha = pm.Determinstic('alpha', kappa/rhoCp)
SE = ComplicatedModel(alpha, data) # Squared error between model output and data
pm.Potential('res', -0.5*SE/sig**2)
```

If `alpha`

is greater than or equal to 0.5, the `ComplicatedModel()`

is unstable and returns NaNs. So is there a way for me to restrict `alpha<0.5`

without causing the sampler too much grief? I suppose I could use `theano ifelse`

or `theano maximum`

like the following:

```
from theano import tensor as tt
from theano.ifelse import ifelse
with pm.Model() as myModel:
sig = pm.HalfNormal('sig', sd=2)
kappa = pm.Normal('kappa', mu=1, sd=1)
rhoCp = pm.Normal('rhoCp', mu=4, sd=1)
a = kappa/rhoCp
alpha = pm.Determinstic('alpha', ifelse(tt.lt(a, 0.5), a, 0.5))
# OR
alpha = pm.Determinstic('alpha', tt.maximum(kappa/alpha, 0.5))
```

However, is the above likely to mess with the nice geometry of the parameter space and make sampling using NUTS difficult?