## models.pseudo_bma_weights()


Pseudo-BMA weights, `w_k` proportional to `exp(elpd_k)`; with


Usage


``` python
models.pseudo_bma_weights(
    lpd,
    bootstrap=True,
    n_draws=1000,
    seed=0,
)
```


`bootstrap=True`, pseudo-BMA+ weights averaged over Bayesian-bootstrap replicates of the observations, which keeps a model that is only slightly better from taking all the weight.


## Parameters


`lpd: list of list of float`  
One list per model, as for [stacking_weights](models.stacking_weights.md#prospicio.models.stacking_weights).

`bootstrap: bool = ``True`  

`n_draws: int = ``1000`  
Bootstrap replicates.

`seed: int = ``0`  
Replicate [b](pricing.Mbbefd.md#prospicio.pricing.Mbbefd.b) uses stream [b](pricing.Mbbefd.md#prospicio.pricing.Mbbefd.b) of [seed](aggregate.EventSet.md#prospicio.aggregate.EventSet.seed).


## Returns


`list of float`