5.4 Calculating predictive accuracy with ArviZ

Fortunately, calculating WAIC and LOO with ArviZ is very simple. We just need to be sure that the Inference Data has the log-likelihood group. When computing a posterior with PyMC, this can be achieved by doing pm.sample(idata_kwargs="log_likelihood": True). Now, let’s see how to compute LOO:

Code 5.3

az.loo(idata_l)

Computed from 8000 posterior samples and 33 observations log-likelihood matrix.

         Estimate       SE elpd_loo   -14.31     2.67
p_loo        2.40        -
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Pareto k diagnostic values:
                         Count...