Hi, My current models is as follows; with pm.Model() as model: l = pm.Gamma("l", alpha=2, beta=1) offset = pm.Gamma("offset", alpha=2, beta=1) nu = pm.HalfCauchy("nu", beta=1) cov = nu ** 2 * pm.gp.cov.Polynomial(X.shape[1], l, 2, offset) gp = pm.gp.Marginal(cov_func=cov) sigma = p…

So what you’re doing here is drawing full samples from the GP, then calculating the pointwise mean and std: pred_samples = pm.sample_posterior_predictive(map_trace, vars=[f_pred], samples=2000) y_pred_custom, uncer = pred_samples['f_pred'].mean(axis=0), pred_samples['f_pred'].std(axis=0) This is…

Predicting on new data with gp.conditional

Questions

Ravindu_Fernando November 9, 2020, 5:23am 2

junpenglao
Read the forum a bit and thought you’d have some input on this. I just want to use this same model to get predictions over and over again. Do you have any thoughts on how to go about this? Thanks

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Predicting on new data with gp.conditional

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