This Python code generates a 10_000 vector `xt`

given a vector of values `mean`

and a scalar variance `v`

.

```
x0 = np.random.normal(loc = 0, scale = 1, size = 10000) // initial points, outputs a 10_000 vector
mean = X0*np.exp(-0.5) // 10_000 vector
variance = np.sqrt(4/(1-np.exp(-1)) // = 1.5901201952413002
xt = np.random.normal(m,v) // = 10_000 vector
```

In statsrs, there is a type mismatch (understandably so!) when inserting a vector into the argument `mean`

in `statsrs::distribution::Normal`

. Are there any work-arounds for this?

My Rust code (which does not work, as there is a type mismatch) is below.

```
let x0: Vec<f64> = thread_rng().sample_iter(Standard).take(size).collect();
// Calculate the endpoint using the initial point, x0.
// Since f64 implements the Copy trait,
// we don't have to clone into a new place in the heap
let endpoint_mean: Vec<_> = x0
.iter()
.map(|&x| THETA + (x - THETA) * (-KAPPA * TIME).exp())
.collect();
let endpoint_variance: f64 =
(SIGMA.pow(2) as f64 / (2.0 * KAPPA) * (1.0 - (-2.0 * KAPPA * TIME).exp())).sqrt();
let n = Normal::new(endpoint_mean, endpoint_variance).unwrap();
println!("{}", n.pdf(1.0))
```