# WAICResult


Result of WAIC computation on a fitted GAM.


Usage

``` python
WAICResult(
    elpd_waic,
    se_elpd_waic,
    p_waic,
    waic,
    pointwise,
)
```


## Attributes


`elpd_waic: float`  
Expected log pointwise predictive density, summed over observations. Higher is better.

`se_elpd_waic: float`  
Approximate standard error of `elpd_waic`, computed as `sqrt(n * var(pointwise))`.

`p_waic: float`  
Effective number of parameters (WAIC penalty), computed as the sum of the per-observation variance of the log-likelihood across posterior draws.

`waic: float`  
The WAIC value on the deviance scale: `-2 * elpd_waic`. Lower is better.

`pointwise: NDArray`  
Per-observation ELPD contributions, shape `(n,)`.