Hello!

I am new to PyMC (and getting reacquainted with Python after a long time of using R and Matlab for data analysis), and I’m trying to use a Gaussian Process (GP) model for interpolation. I have values spaced randomly in 2D, and I want to fit a GP model to predict values between them.

I have code to specify my model (based on the GP example here), and I’m able to fit the model using NUTS, but I receive the following error when I use the `pymc.gp.Marginal.conditional`

function to specify the new points in 2D at which I want to predict values:

**TypeError: Unsupported dtype for TensorType: object**

(to keep the post short, the full error trace is in this attached text file:

error_traceback.txt (3.3 KB))

I found one other post on Discourse with this error message, but that seemed to be due to mixing Aesara and Theano code. As you can see in my reproducible example below, I am not doing this. Does anyone have an idea of what could be the issue? Thanks in advance for your help!

Package versions:

- numpy: 1.24.2
- pandas: 1.5.3
- PyMC: 5.2.0
- pytensor: 2.10.1

```
import numpy as np
import pandas as pd
import pymc as pm
from numpy.random import default_rng
from math import pi
'''
PREPARE DATA
'''
# In my case, data are loaded using `pd.read_csv`, but this chunk generates a similar pandas data frame.
n = 50
expol = np.random.uniform(-pi/2, 5*pi/2, size=n)
x_disk = np.random.uniform(size=n)
y_disk = np.random.uniform(size=n)
df = pd.DataFrame(data=np.column_stack((expol, x_disk, y_disk)),
index=np.random.choice(10000, n, replace=False),
columns=["expol", "x_disk", "y_disk"])
# Extract just the x and y values as an array
xy = df[["x_disk", "y_disk"]].values
'''
SET UP THE MODEL
'''
with pm.Model() as gp_fit:
# Covariance parameters
rho = pm.HalfCauchy('rho', 5)
eta = pm.HalfCauchy('eta', 5)
# Mean and convariance
M = pm.gp.mean.Zero()
K = (eta ** 2) * pm.gp.cov.ExpQuad(2, rho)
# Measurement noise
sigma = pm.HalfNormal('sigma', 50)
# Instantiate the GP and provide data for evaluating likelihood
recruit_gp = pm.gp.Marginal(mean_func=M, cov_func=K)
recruit_gp.marginal_likelihood('recruits', X=xy, y=df['expol'], sigma=sigma)
'''
FIT THE MODEL
'''
with gp_fit:
trace = pm.sample(target_accept=0.95, chains=4, return_inferencedata=True, cores=1)
'''
SAMPLE FROM THE POSTERIOR AT NEW LOCATIONS
'''
# Specify the points at which to generate predictions
x_new = np.linspace(np.min(df["x_disk"]), np.max(df["x_disk"]), 10)
y_new = np.linspace(np.min(df["y_disk"]), np.max(df["y_disk"]), 10)
xs, ys = np.asarray(np.meshgrid(x_new, y_new))
xy_new = np.asarray([xs.ravel(), ys.ravel()]).T
with gp_fit:
# Specify the new locations (this is where the error is thrown)
expol_pred = recruit_gp.conditional("expol_pred", Xnew=xy_new)
# Sample from the posterior at the new locations
gp_expol_samples = pm.sample_posterior_predictive(trace, vars=[expol_pred], samples=3, random_seed=42)
```