# Multidimensional gaussian process

**URL:** https://discourse.pymc.io/t/multidimensional-gaussian-process/907
**Category:** Questions
**Created:** [March 6, 2018, 10:00pm UTC](https://discourse.pymc.io/t/multidimensional-gaussian-process/907 "2018-03-06T22:00:04Z")
**Posts on this page:** 1
**Showing post:** 4

<div class="post-metadata">

### Author: ![bwengals](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/bwengals/32/1237_2.png) [@bwengals](https://discourse.pymc.io/u/bwengals)
#### Post date: [March 7, 2018, 4:31am UTC](https://discourse.pymc.io/t/multidimensional-gaussian-process/907/4 "2018-03-07T04:31:42Z")

</div>

```
params = np.array([[1.0, -2.4, 3.6, 1.3]])

```

(one more pair of brackets)

which has 1 row and 4 columns. Also like I’d suggest a couple other things that might be helpful,

- Use `gp.Marginal`, since the likelihood is MvNormal it’s conjugate to the GP. You’ll get a big speed up. If you wish to use `gp.Latent` with that likelihood, since your covariance for `MvNormal` is diagonal, you can use `Normal` instead which will be more efficient.
- Set njobs=1 in the `pm.sample(...)` call. The matrix operations used by Theano here are multithreaded, so running multiple chains simultaneously bogs things down.
- There should be no need to set `start=model.test_point` if everything is specified properly (should be `gp_model.test_point` I think).

---

_[View the full topic](https://discourse.pymc.io/t/multidimensional-gaussian-process/907)._