Hi pymc devs and all,

I’d like to be able to perform the “inverse” transform on model variables that have been automatically transformed. (For example, sometimes I’d like to be able to calculate a model’s `logp`

by passing in model parameters from the untransformed space.)

After some digging around, the approach that I’ve found is to access the transform object via the transformed tensorvariable’s “tag” attribute. I’m not 100% sure if this is the recommended way to do this, so I’d appreciate feedback if there is a less hacky solution. For example, we can do the following:

```
with pm.Model() as m:
x = pm.Beta("x", alpha=1, beta=1)
m.value_vars[0] # x_logodds__
m.value_vars[0].tag.transform.backward(0).eval() # array(0.5, dtype=float32)
with pm.Model() as m:
x = pm.Gamma("x", alpha=1, beta=1)
m.value_vars[0] # x_log__
m.value_vars[0].tag.transform.backward(np.log(1.2)).eval() # array(1.2)
```

However, this approach is failing with variables from a BoundedContinuous distribution. (E.g. uniform, truncatednormal.) It appears that the values in `bound_args_indices`

is out of bounds when the transform object tries to access the variable’s bound information:

```
with pm.Model() as m:
x = pm.Uniform("x", lower=1, upper=2)
m.value_vars[0] # x_interval__
m.value_vars[0].tag.transform.backward(5).eval()
```

```
IndexError Traceback (most recent call last)
test.ipynb Cell 2 in <cell line: 14>()
12 x = pm.Uniform("x", lower=1, upper=2)
13 m.value_vars[0] # x_interval__
---> 14 m.value_vars[0].tag.transform.backward(5).eval()
File .../lib/python3.10/site-packages/aeppl/transforms.py:446, in IntervalTransform.backward(self, value, *inputs)
445 def backward(self, value, *inputs):
--> 446 a, b = self.args_fn(*inputs)
448 if a is not None and b is not None:
449 sigmoid_x = at.sigmoid(value)
File .../lib/python3.10/site-packages/pymc/distributions/continuous.py:175, in bounded_cont_transform.<locals>.transform_params(*args)
173 lower, upper = None, None
174 if bound_args_indices[0] is not None:
--> 175 lower = args[bound_args_indices[0]]
176 if bound_args_indices[1] is not None:
177 upper = args[bound_args_indices[1]]
IndexError: tuple index out of range
```

Because I’m not 100% sure whether or not I’m doing the correct thing, I wanted to ask this in the discourse. Any help would be much appreciated. Thanks!

–

Edit: I’ve also noticed that the same error appears when calling `transform.forward()`

from an automatically-transformed variable’s transform. I’m now curious—if this method errors out when performing the forward-transform, how/where does pymc calculate the transformation?