Hello,

Sorry if this is a very easy solution - I searched for a few hours and wasn’t able to find a solution that I understood.

I was wondering if there was a way to introduce uncertainty in a parameter that is being used in the model, without adding it to the chain. For example, suppose I have a linear relationship between two parameters `x`

and `y`

, such that

`y = Ax + B`

, where `A = -0.006 +/- 0.001`

and `B = 1 +/- 0.04`

. I am sampling `x`

and want to change `A`

and `B`

each ~~chain~~ sample so I can incorporate the uncertainty in my input parameters into the model uncertainty. Is there a way to do this? Thanks.

EDIT: Sorry all, it looks like my phrasing above was confusing. In pseudo-code, here’s what I want:

```
import numpy as np
import pymc3 as pm
# Dummy data
Y = np.arange(0, 50) + np.random.randn(50)
for sample in int(nburnin + nsamples):
with pm.Model() as model:
A = np.random.normal(-0.006, 0.001)
B = np.random.normal(1.0, 0.04)
y_true = pm.Data('y', Y)
x = pm.Flat('x')
y_syn = A*x + B
#... augment likelihood to look at difference between y_true and y_syn ...
trace = pm.sample(...)
```

Of course the above code wouldn’t work the way it is intended with PyMC3, but this demonstrates my point visually, I think.

I don’t want to use `pm.Normal('A', mu=-0.006, sigma=0.001`

and `pm.Normal('B', mu=1, sigma=0.04)`

because (my understanding) is that this would sample those parameters to solve them in the MCMC. I just want the values *and their uncertainties* added into my model solving for x.