## models.simulate_from_means()


Joint predictive draws from fitted means, for engines that give only


Usage


``` python
models.simulate_from_means(
    family,
    means,
    n_sims,
    seed,
    dispersion=None,
    weights=None,
    theta=None,
    power=None
)
```


a mean per row (the boosting adapters): the family adds process noise and several mean vectors (bootstrap refits) add parameter uncertainty.

Simulation `i` uses stream `i` of [seed](aggregate.EventSet.md#prospicio.aggregate.EventSet.seed): it picks one mean vector uniformly, then draws each row's response from the family with that mean, the row's dispersion and the row's weight. Components are keyed `row = 0, 1, ...`, as [GlmFit.predict_distribution](models.GlmFit.md#prospicio.models.GlmFit.predict_distribution) keys them.


## Parameters


`family: str`  
As in [Glm](models.Glm.md#prospicio.models.Glm).

`means: list of list of float`  
One or more mean vectors, one value per row each.

`n_sims: int`  

`seed: int`  

`dispersion: float or list of float = None`  
One value for every row, or one per row (from a dispersion model); 1 by default.

`weights: list of float = None`  
Prior weights; 1 by default.

`theta: float = None`  
Negative binomial [theta](reserving.ClarkFit.md#prospicio.reserving.ClarkFit.theta), Tweedie [power](distributions.Tweedie.md#prospicio.distributions.Tweedie.power).

`power: float = None`  
Negative binomial [theta](reserving.ClarkFit.md#prospicio.reserving.ClarkFit.theta), Tweedie [power](distributions.Tweedie.md#prospicio.distributions.Tweedie.power).


## Returns


`PredictiveDistribution`  


## Examples

``` python
>>> from prospicio.models import simulate_from_means
>>> pd = simulate_from_means("poisson", [[0.1, 0.4]], 20_000, 7)
>>> round(pd.mean(), 1)
```

0.5