Thank you. I still run into an error. Here is my code:

```
radarspy_path = r"I:\projects\RadSPy-1DRadarSimulator"
sys.path.append(radarspy_path)
from RadSPy_op import simulator_1D
def my_model(epsilon_r, loss_tangent, thicknesses, dB_noise)
...
The simulation function use the parameters given and returns an ndarray
...
return np.real(result[start_index:end_index, 1] )
def my_loglike(epsilon_r, loss_tangent, thicknesses, dB_noise, data):
for param in (epsilon_r, loss_tangent, thicknesses, dB_noise, data):
if not isinstance(param, (float, np.ndarray)):
raise TypeError(f"Invalid input type to loglike: {type(param)}")
model = my_model(epsilon_r, loss_tangent, thicknesses, dB_noise)
return -0.5 * ((data - model) / np.abs(dB_noise)) ** 2 - np.log(np.sqrt(2 * np.pi)) - np.log(np.abs(dB_noise))
class LogLike(Op):
def make_node(self, epsilon_r, loss_tangent, thicknesses, dB_noise, data) -> Apply:
# Convert inputs to tensor variables
epsilon_r = pt.as_tensor(epsilon_r)
loss_tangent = pt.as_tensor(loss_tangent)
thicknesses = pt.as_tensor(thicknesses)
dB_noise = pt.as_tensor(dB_noise)
data = pt.as_tensor(data)
inputs = [epsilon_r, loss_tangent, thicknesses, dB_noise, data]
outputs = [data.type()]
return Apply(self, inputs, outputs)
def perform(self, node: Apply, inputs: list[np.ndarray], outputs: list[list[None]]) -> None:
epsilon_r, loss_tangent, thicknesses, dB_noise, data = inputs # this will contain my variables
loglike_eval = my_loglike(epsilon_r, loss_tangent, thicknesses, dB_noise, data)
outputs[0][0] = np.asarray(loglike_eval)
```

```
# set up our data
true_epsilon_r = np.array([8.8, 5.0, 7.0])
true_loss_tangent = np.array([0.01, 0.02, 0.03])
true_thickness = np.array([30, 40])
dBnoise = -40
synthetic_data = my_model(true_epsilon_r, true_loss_tangent, true_thickness, dBnoise)
# create our Op
loglike_op = LogLike()
test_out = loglike_op(true_epsilon_r, true_loss_tangent, true_thickness, -100, synthetic_data)
def custom_dist_loglike(data, true_epsilon_r, true_loss_tangent, true_thickness, dB_noise):
# data, or observed is always passed as the first input of CustomDist
return loglike_op(true_epsilon_r, true_loss_tangent, true_thickness, dB_noise, data)
# use PyMC to sampler from log-likelihood
with pm.Model() as no_grad_model:
# uniform priors on epsilon_r
eps = pm.Uniform("eps", lower=3.0, upper=10.0, shape=3)
# use a CustomDist with a custom logp function
likelihood = pm.CustomDist("likelihood", eps, true_loss_tangent, true_thickness, dBnoise, observed=synthetic_data, logp=custom_dist_loglike)
trace = pm.sample(5000, tune=1000, progressbar=True, return_inferencedata=True)
```

The error reads in likelihood = pm.CustomDist(“likelihood”, eps, true_loss_tangent, true_thickness, dBnoise, observed=synthetic_data, logp=custom_dist_loglike)

ValueError: Could not broadcast dimensions. Incompatible shapes were [(ScalarConstant(ScalarType(int64), data=3),), (ScalarConstant(ScalarType(int64), data=3),), (ScalarConstant(ScalarType(int64), data=2),), (ScalarConstant(ScalarType(int64), data=1),)]

It seems that the **CustomDist** function cannot handle the input parameter correctly, as the example given in `PyTensor Op without gradients`

`Model definition`

Using a “black box” likelihood function — PyMC example gallery