# Pymc conditioning on an observed

I am showing participants a scatter plot of x and y and then asking them to guess the correlation. I am trying to model how they arrive at their estimates. For simplicity, our baseline model assumes uncertainty in the perception component only (there is no parameter estimation uncertainty), assuming that observers compute a noisy representation of points’ positions in the two-dimensional scene.

So, given data = (user_estimated_correlation,x,y) where x,y are the actual data points and user_estimated_correlation is the participants guess of the correlation, I would like to infer the noise level added by the uncertainty in the perception.

def perception_model(data):
user_estimated_correlation,x,y=data

noise_in_x = pm.Normal('mu_x', 0,x.max()-x.min(), size=x.shape[0])
noise_in_y = pm.Normal('mu_y', 0,y.max()-y.min(), size=y.shape[0])

@pm.deterministic