## models.elpd_loo()


Leave-one-out cross-validation by Pareto-smoothed importance sampling


Usage


``` python
models.elpd_loo(
    log_lik,
    r_eff=None,
)
```


(PSIS-LOO), from one fit's pointwise log-likelihood draws. Matches the R package [loo](models.BayesGlmFit.md#prospicio.models.BayesGlmFit.loo).


## Parameters


`log_lik: list of list of float`  
One row per posterior draw, one column per observation: `log p(y_i | theta_s)`.

`r_eff: list of float = None`  
Relative efficiency of the draws per observation (1 for independent draws).


## Returns


`Elpd`  
With [pareto_k](models.Elpd.md#prospicio.models.Elpd.pareto_k) and [k_threshold](models.Elpd.md#prospicio.models.Elpd.k_threshold).