## models.Elpd


An ELPD estimate from [elpd_loo](models.elpd_loo.md#prospicio.models.elpd_loo) or [elpd_waic](models.elpd_waic.md#prospicio.models.elpd_waic).


Usage


``` python
models.Elpd()
```


## Attributes

| Name | Description |
|----|----|
| [elpd](#elpd) | Expected log pointwise predictive density, summed. |
| [ic](#ic) | The information criterion, `-2 elpd` (LOOIC or WAIC). |
| [k_threshold](#k_threshold) | PSIS-LOO only: `min(1 - 1/log10(S), 0.7)`; observations with a |
| [p](#p) | Effective number of parameters, `lppd - elpd`. |
| [pareto_k](#pareto_k) | PSIS-LOO only: the fitted Pareto shape per observation. |
| [pointwise](#pointwise) | ELPD per observation. |
| [se](#se) | Its standard error, `sqrt(N var(pointwise))`. |

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#### elpd


Expected log pointwise predictive density, summed.


`elpd: float`


------------------------------------------------------------------------


#### ic


The information criterion, `-2 elpd` (LOOIC or WAIC).


`ic: float`


------------------------------------------------------------------------


#### k_threshold


PSIS-LOO only: `min(1 - 1/log10(S), 0.7)`; observations with a


`k_threshold: float | None`


larger [pareto_k](models.Elpd.md#prospicio.models.Elpd.pareto_k) are unreliable.


------------------------------------------------------------------------


#### p


Effective number of parameters, `lppd - elpd`.


`p: float`


------------------------------------------------------------------------


#### pareto_k


PSIS-LOO only: the fitted Pareto shape per observation.


`pareto_k: list[float] | None`


------------------------------------------------------------------------


#### pointwise


ELPD per observation.


`pointwise: list[float]`


------------------------------------------------------------------------


#### se


Its standard error, `sqrt(N var(pointwise))`.


`se: float`