I’m trying to model my data with an equation that has a logarithm:

`y = A+B*log10(x-C)`

x- is a predictor variable

A, B & C are parameters I want to predict with a posterior. The model works if I use a constant value for C, but once I replace it with a prior probability, it breaks down. (I even truncated the C prior distribution, below the minimum value of x so not to have a negative in the log10().)

MY CODE:

```
curve_model = pm.Model()
with curve_model:
# Define Prior Probabilities
A = pm.Normal('A', mu=7.4, sd=2)
B = pm.Normal('B', mu=-2, sd=2)
C = pm.TruncatedNormal('C', mu=34, sd=2, upper=38)
sd_y = pm.InverseGamma('t', mu=0.5, sd=0.25)
mu_y = A+B*np.log10(x - C)
# Write parameters for posterior
post_fatigue = pm.Normal('post_fatigue', mu=mu_y , sd=sd_y, observed=y)
```

MY ERROR MSG:

```
ValueError: length not known: Elemwise{sub,no_inplace} [id A] ''
|TensorConstant{[38.5 38..47 93.443]} [id B]
|InplaceDimShuffle{x} [id C] ''
|ViewOp [id D] 'C'
|Elemwise{sub,no_inplace} [id E] ''
|TensorConstant{38} [id F]
|Elemwise{exp,no_inplace} [id G] ''
|C_upperbound__ [id H]
```

I’m sure the problem has to do with getting the difference of a data set and a distribution. I just don’t know how to work around this.

Thanks!