## risk.StudentTCopula


The Student t copula with correlation matrix `correlation` and [nu](risk.StudentTCopula.md#prospicio.risk.StudentTCopula.nu)


Usage


``` python
risk.StudentTCopula()
```


degrees of freedom: Gaussian-like correlation with joint extremes.


## Parameters


`correlation: list of list of float`  
Symmetric, unit diagonal, positive definite.

`nu: float`  
Degrees of freedom, positive.


## Raises


`ValueError`  
If the matrix or [nu](risk.StudentTCopula.md#prospicio.risk.StudentTCopula.nu) is invalid.


## Examples

``` python
>>> from prospicio.risk import StudentTCopula
>>> StudentTCopula([[1.0, 0.5], [0.5, 1.0]], 4.0).dim
```

2


## Attributes

| Name | Description |
|----|----|
| [dim](#dim) | Number of dimensions. |
| [nu](#nu) | Degrees of freedom. |

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


#### dim


Number of dimensions.


`dim: int`


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


#### nu


Degrees of freedom.


`nu: float`


## Methods

| Name | Description |
|----|----|
| [sample()](#sample) | [n](distributions.Binomial.md#prospicio.distributions.Binomial.n) draws of uniforms; draw `i` uses stream `i` of [seed](aggregate.EventSet.md#prospicio.aggregate.EventSet.seed). |

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


#### sample()


[n](distributions.Binomial.md#prospicio.distributions.Binomial.n) draws of uniforms; draw `i` uses stream `i` of [seed](aggregate.EventSet.md#prospicio.aggregate.EventSet.seed).


Usage


``` python
sample(n, seed)
```


##### Parameters


`n: int`  

`seed: int`  


##### Returns


`list of list of float`