I’m trying to fit an AR1 a time-series as following: with pm.Model() as ar1_mat1: theta = pm.Normal('theta',mu= 0, sigma = 3.0) tau = pm.HalfNormal('tau',sigma =1) likelihood = pm.AR1('likelihood', k=theta, tau_e = tau, observed = y_standard.values) trace = pm.sample(1000,…

If you want to generate posterior predictions “by hand”, then you don’t need sample_posterior_predictive(). You can just unpack parameter values from the posterior returned by pm.sample(). Then plug them back into your model as you describe. If you haven’t already, you should check out the AR1 no…

Pm.sample_posterior_predictive() from an AR1 process

Questions

cluhmann January 24, 2022, 4:03pm 3

Where/how did you get likelihood you showed?

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Pm.sample_posterior_predictive() from an AR1 process

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