## models.BayesStacking


Bayesian stacking: a posterior for the stacking weights, with a


Usage


``` python
models.BayesStacking()
```


Dirichlet prior, sampled by NUTS from pointwise held-out log densities (Yao et al., 2018). [stacking_weights](models.stacking_weights.md#prospicio.models.stacking_weights) gives the optimum alone.


## Parameters


`concentration: list of float`  
Dirichlet concentration, one per model (default 1, uniform).

`chains: int = 4, 1000, 1000`  

`tune: int = 4, 1000, 1000`  

`draws: int = 4, 1000, 1000`  

`seed: int = ``0`  


## Methods

| Name | Description |
|----|----|
| [fit()](#fit) | Samples the weights. |

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


#### fit()


Samples the weights.


Usage


``` python
fit(lpd)
```


##### Parameters


`lpd: list of list of float`  
One list per model, one held-out log density per observation.


##### Returns


`StackingFit`