I am fairly new to PyMC3 and I am trying to model the uncertainty propagation during the calibration process of a sensor. As such, the outputs of my model are [Y] and the inputs are [X]. xdata and ydata are the np.arrays containing my calibration data. My model looks` like this: def volt_est(w,x): …

Hi Santiago- Looks to me like you’re calibrating a model in terms of P(Y|X) but then want to sample from P(X|Y). This is doable since the joint distribution P(X,Y) is what’s modeled, but sampling from a pre-image is not as straightforward as posterior prediction. I can think of two ways to do this. …

Making predictions on model inputs based on observed outputs

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Santiago March 18, 2021, 12:19am 3

Hi Chris,

This is perfect, thanks. Since my calibration function is a one-to-one I ended up using your third recommendation because it was the fastest for me.

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Making predictions on model inputs based on observed outputs

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