## models.stacking_weights()


Stacking weights from pointwise held-out log predictive densities


Usage


``` python
models.stacking_weights(lpd)
```


(Yao et al., 2018): the weights on the simplex that maximize the log score of the mixture of the models' predictive distributions.

Works for any model: pass PSIS-LOO pointwise values ([Elpd.pointwise](models.Elpd.md#prospicio.models.Elpd.pointwise)) for a Bayesian fit, or cross-validated log densities for any other. A model that adds nothing gets weight exactly 0.


## Parameters


`lpd: list of list of float`  
One list per model, each with one log density per observation.


## Returns


`list of float`  
One weight per model, summing to 1.


## Examples

``` python
>>> from prospicio.models import stacking_weights
>>> w = stacking_weights([[-0.1, -0.1, -3.0, -3.0], [-3.0, -3.0, -0.1, -0.1]])
>>> [round(x, 9) for x in w]
```

\[0.5, 0.5\]