# stacking()


Compute stacking weights for model averaging.


Usage

``` python
stacking(*results)
```


Given LOO or WAIC results from multiple models fitted to the same data, finds the optimal combination weights that maximize the combined leave-one-out predictive density of the weighted mixture.

Unlike pairwise [loo_compare()](loo_compare.md#whittaker.loo_compare) or [waic_compare()](waic_compare.md#whittaker.waic_compare), stacking handles any number of models simultaneously and produces a single set of weights suitable for prediction averaging.


## Parameters


`*results: LOOResult or WAICResult`  
Two or more LOO or WAIC results. All must be the same type and computed on the same data (same number of observations).


## Returns


`StackingResult`  
Contains the optimal weights, combined ELPD, and a display-friendly summary.


## Examples

``` python
loo1 = model1.loo()
loo2 = model2.loo()
loo3 = model3.loo()
result = stacking(loo1, loo2, loo3)
print(result.weights)  # e.g., array([0.62, 0.35, 0.03])
```