I am attempting to implement bayesian model calibration under the classical Kennedy-O’Hagan framework using PyMC3. Briefly, the setup is as follows: I have n observations z_i of the form z_i = f(x_i, \theta) + g(x_i) + e_i, where x_i are known imputs and \theta are unknown calibration parameters …

Yep, the key is to stack the theano scaler with a numpy array. For example another way to do is import theano import theano.tensor as tt x1shared = theano.shared(x1[:, None]) with pm.Model(): ... input_1 = tt.concatenate([x1shared, tt.tile(theta, len(x1), ndim=2).T], axis=1) ...

Bayesian model calibration with Gaussian Process

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NateAM March 14, 2018, 4:32pm 12

I found a link where someone was working on your same problem with pymc3 and sklearn:
https://github.com/pymc-devs/pymc3/issues/1574

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Bayesian model calibration with Gaussian Process

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