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

Questions

junpenglao March 14, 2018, 3:13pm 8

The cov function in PyMC3 accepts numpy array, theano tensor, or pymc3 RV as input. You can only do inference on the RVs in general (with some twist you can do it on theano tensor as well)

Topic		Replies	Views
Finding posterior for calibration using saved Gaussian model in pymc3 Questions theano	2	1352	November 15, 2017
Multidimensional gaussian process Questions	55	10241	October 22, 2020
Computer model calibration using a Gaussian Process: From stan to pymc v5 gaussian_process	10	725	June 22, 2023
How to calibrate the GP model with various data? Questions	2	798	September 22, 2018
Bayesian calibration on GP model v3 gaussian_process	7	1146	July 23, 2022

Bayesian model calibration with Gaussian Process

Related topics