I am trying to output a posterior probability distribution of parameters using 10000 points extracted from the posterior of a GP that was trained on a small set of simulation data. The prior of the parameters given the outputs is assumed to be non-informative uniform for each parameter. There are 13…

Bayesian Analysis of Posterior Predictions from GP model

AlexAndorra August 3, 2020, 9:15am 9

I think you can just do theta = pm.Uniform("theta", np.array([16.0, 0.4, 6.0]), np.array([18.0, 0.8, 10.2]), shape=3)

Topic		Replies	Views
How to apply the surrogate model to Bayesian method? Questions	1	594	February 8, 2019
Marginal likelihood implementation and measurement uncertainty Questions	12	910	November 2, 2019
Multidimensional gaussian process Questions	55	10241	October 22, 2020
Finding posterior for calibration using saved Gaussian model in pymc3 Questions theano	2	1352	November 15, 2017
Adaptive surrogate models Questions	4	1668	October 23, 2017