## risk.entropic()


Entropic risk measure `(1 / theta) log E[exp(theta X)]`: the certainty


Usage


``` python
risk.entropic(
    dist,
    theta,
)
```


equivalent of a loss under exponential utility.

It rises from the mean (`theta -> 0`) to the largest draw (`theta -> inf`); for a normal loss it is `mu + theta sigma**2 / 2`.


## Parameters


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

`theta: float`  
Risk aversion, positive.


## Returns


`float`  


## Examples

``` python
>>> import math
>>> from prospicio.risk import entropic
>>> round(entropic([0.0, 1.0], math.log(2.0)), 12) == round(math.log2(1.5), 12)
```

True