## distributions.Binomial


Binomial claim counts: [n](distributions.Binomial.md#prospicio.distributions.Binomial.n) risks, each claiming with probability


Usage


``` python
distributions.Binomial()
```


[p](models.Elpd.md#prospicio.models.Elpd.p).


## Parameters


`n: int`  
Number of trials.

`p: float`  
Claim probability in `[0, 1)`.


## Examples

``` python
>>> from prospicio.distributions import Binomial
>>> Binomial(10, 0.3).mean()
```

3.0


## Attributes

| Name | Description |
|----|----|
| [n](#n) | Number of trials. |
| [p](#p) | Claim probability per trial. |

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


#### n


Number of trials.


`n: int`


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


#### p


Claim probability per trial.


`p: float`


## Methods

| Name | Description |
|----|----|
| [cdf()](#cdf) | `P(N <= k)`. |
| [mean()](#mean) | Mean of the claim count. |
| [pmf()](#pmf) | `P(N = k)`. |
| [quantile()](#quantile) | Smallest `k` with `P(N <= k) >= p`. |
| [sample()](#sample) | [n](distributions.Binomial.md#prospicio.distributions.Binomial.n) claim counts from stream `stream` of the generator keyed by |
| [variance()](#variance) | Variance of the claim count. |

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


#### cdf()


`P(N <= k)`.


Usage


``` python
cdf(k)
```


##### Parameters


`k: int`  


##### Returns


`float`  


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


#### mean()


Mean of the claim count.


Usage


``` python
mean()
```


##### Returns


`float`  


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


#### pmf()


`P(N = k)`.


Usage


``` python
pmf(k)
```


##### Parameters


`k: int`  


##### Returns


`float`  


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


#### quantile()


Smallest `k` with `P(N <= k) >= p`.


Usage


``` python
quantile(p)
```


##### Parameters


`p: float`  


##### Returns


`int`  


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


#### sample()


[n](distributions.Binomial.md#prospicio.distributions.Binomial.n) claim counts from stream `stream` of the generator keyed by


Usage


``` python
sample(n, seed, stream=0)
```


[seed](aggregate.EventSet.md#prospicio.aggregate.EventSet.seed).


##### Parameters


`n: int`  

`seed: int`  

`stream: int = ``0`  


##### Returns


`list of int`  


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


#### variance()


Variance of the claim count.


Usage


``` python
variance()
```


##### Returns


`float`