# Welcome to GPJax!

## Contents

# Welcome to GPJax!#

GPJax is a didactic Gaussian process library that supports GPU acceleration and just-in-time compilation. We seek to provide a flexible API as close as possible to how the underlying mathematics is written on paper to enable researchers to rapidly prototype and develop new ideas.

You can view the source code for GPJax here on Github.

## ‘Hello World’ example#

Defining a Gaussian process posterior is as simple as typing the maths we would write on paper. To see this, consider the following example.

```
import gpjax as gpx
kernel = gpx.kernels.RBF()
prior = gpx.gps.Prior(kernel = kernel)
likelihood = gpx.likelihoods.Gaussian(num_datapoints = 123)
posterior = prior * likelihood
```

Note

If you’re new to Gaussian processes and want a gentle introduction, we have put together an introduction to GPs notebook that starts from Bayes’ theorem and univariate Gaussian random variables. The notebook is linked here.

See also

To learn more, checkout the regression notebook.

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