Hi

I am new to pymc and to wanted to implement an easy example of histogram data

with an exponential shape that is convolved with a gaussian kernel. For the convolution I’m using the function shown here How to apply signal filtering in PyMC? but when I run my code

```
import pytensor
import pytensor.tensor as pt
# Generate toy data
xlim = (0, 3000)
bin_width = 2
bins = np.arange(xlim[0], xlim[1], bin_width)
bin_midpoints = (bins[1:] + bins[:-1]) / 2
A_true = 1e4
tau_true = 1e-3
counts = A_true * np.exp(-tau_true * bin_midpoints)
counts += np.random.randn(counts.shape[0])*100
# Create Gaussian kernel
kernel_gaussian_range = 5
kernel_sigma_mean = 3.3
kernel_sigma_std = 0.1
kernel_range = np.arange(-kernel_gaussian_range*kernel_sigma_mean, kernel_gaussian_range*kernel_sigma_mean, bin_width)
kernel_values = np.exp((-kernel_range ** 2) / (2 * kernel_sigma_mean ** 2))
kernel_values /= np.sum(kernel_values)
observed_counts = np.convolve(counts, kernel_values, mode='same')
def conv_1d(idx, data, kernel):
return (data[idx:idx+len(kernel_range)] * kernel).sum()
with pm.Model() as model:
# Priors for the parameters
A_log = pm.Normal('A_log', mu=np.log(A_true)/2, sigma=0.1)
tau_log = pm.Normal('tau_log', mu=np.log(tau_true)/2, sigma=0.1)
sigma = pm.Normal('sigma', mu=kernel_sigma_mean, sigma=kernel_sigma_std) # Sigma of the Gaussian kernel
# Transform parameters back to original scale
A = pm.Deterministic('A', pm.math.exp(A_log))
tau = pm.Deterministic('tau', pm.math.exp(tau_log))
# Generate Gaussian kernel based on sampled sigma
kernel = pm.Deterministic('kernel', pm.math.exp(-pm.math.sqr(kernel_range) / (2 * pm.math.sqr(sigma))))
kernel /= pm.math.sum(kernel) # Normalize the kernel
expected_counts = A * pm.math.exp(-tau * bin_midpoints)
convolved_counts = pt.reshape(
pt.conv.conv2d(
expected_counts.reshape((1, 1, 1, expected_counts.shape[0])),
kernel.reshape((1, 1, 1, kernel.shape[0])),
border_mode="valid"
), (expected_counts.shape[0] - (kernel.shape[0] - 1),)
).eval()
# convolved_counts, _ = pytensor.scan(conv_1d,
# sequences = pytensor.tensor.arange(len(observed_counts) - len(kernel_range) + 1),
# non_sequences=[expected_counts, kernel])
# Likelihood (sampling distribution) of the observed data
counts_likelihood = pm.Poisson('counts_likelihood', mu=convolved_counts, observed=observed_counts)
# Run the MCMC sampling
trace = pm.sample(2000, tune=2000, cores=4)
```

I get the error ValueError: Incompatible Elemwise input shapes [(1499,), (1483,)]. I also tried using the scan function as you can see in the code but I get the same error, which is even more confusing to me since this functions works just fine if I compile it with `pymc_convolution = pytensor.function(inputs=[data, kernel], outputs=rolling_average)`

and pass it two numpy arrays.

Any help would be greatly appreciated