## risk.esscher()


Esscher premium `E[X exp(h X)] / E[exp(h X)]`: the mean after tilting


Usage


``` python
risk.esscher(
    dist,
    h,
)
```


probability towards large losses.

The mean at `h = 0`; `mu + h sigma**2` for a normal loss.


## Parameters


`dist: Sampled, PredictiveDistribution or list of float`  
A predictive distribution is measured on its total.

`h: float`  


## Returns


`float`  


## Examples

``` python
>>> import math
>>> from prospicio.risk import esscher
>>> round(esscher([0.0, 1.0], math.log(3.0)), 12)
```

0.75