I have been reading this topic and the linked posts within and I want to use the Potential function to scale the logp according to the uncertainty in the observed data.

I am trying to adapt this example :

```
import numpy as np
import pymc3 as pm
import matplotlib.pyplot as plt
# set random seed for reproducibility
np.random.seed(12345)
x = np.arange(5,400,10)*1e3
# Parameters for gaussian
amp_true = 0.2
size_true = 1.8
ps_true = 0.1
#Gaussian function
gauss = lambda x,amp,size,ps: amp*np.exp(-1*(np.pi**2/(3600.*180.)*size*x)**2/(4.*np.log(2.)))+ps
f_true = gauss(x=x,amp=amp_true, size=size_true, ps=ps_true )
# add noise to the data points
noise = np.random.normal(size=len(x)) * .02
f = f_true + noise
f_error = np.ones_like(f_true) * 0.05 * f.max()
with pm.Model() as model3:
amp = pm.Uniform('amp', 0.05, 0.4, testval= 0.15)
size = pm.Uniform('size', 0.5, 2.5, testval= 1.0)
ps = pm.Normal('ps', 0.13, 40, testval=0.15)
gauss = pm.Deterministic('gauss',amp*np.exp(-1*(np.pi**2*size*x/(3600.*180.))**2/(4.*np.log(2.)))+ps)
y = pm.Normal('y', mu=gauss, sd=f_error, observed=f)
trace=pm.sample(2000)
# extract and plot results
y_min = np.percentile(trace.gauss,2.5,axis=0)
y_max = np.percentile(trace.gauss,97.5,axis=0)
y_fit = np.percentile(trace.gauss,50,axis=0)
plt.plot(x,f_true,'b', marker='None', ls='-', lw=1, label='True')
plt.errorbar(x,f,yerr=f_error, color='r', marker='.', ls='None', label='Observed')
plt.plot(x,y_fit,'k', marker='+', ls='None', ms=5, mew=1, label='Fit')
plt.fill_between(x, y_min, y_max, color='0.5', alpha=0.5)
plt.legend()
plt.show()
```

which results in

However, when I try to define a model using

```
# add noise to the data points
noise = np.random.normal(size=len(x)) * .02
f = f_true + noise
f_error = np.ones_like(f_true) * 0.05 * f.max()
w = 1.0 / f_error**2
y_sd = np.ones_like(f_true)
with pm.Model() as model3:
amp = pm.Uniform('amp', 0.05, 0.4, testval= 0.15)
size = pm.Uniform('size', 0.5, 2.5, testval= 1.0)
ps = pm.Normal('ps', 0.13, 40, testval=0.15)
gauss = pm.Deterministic('gauss',amp*np.exp(-1*(np.pi**2*size*x/(3600.*180.))**2/(4.*np.log(2.)))+ps)
y_ = pm.Potential('Y_obs', w * pm.Normal('y', mu=gauss, sd=y_sd).logp(f))
trace = pm.sample(2000)
```

I get this error:

Traceback (most recent call last):

File “/home/user/PycharmProjects/weighted_inference.py”, line 46, in

y_ = Potential(‘Y_obs’, w * Normal(‘y’, mu=gauss, sd=y_sd).logp(f))TypeError: For compute_test_value, one input test value does not have the requested type.

The error when converting the test value to that variable type:

Wrong number of dimensions: expected 0, got 1 with shape (40,).

I wonder if anyone could please tell me the right way to do it.