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[ICML'18] Scalable Gaussian Processes with Grid-Structured Eigenfunctions

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# `gp_grief`
Large Scale Gaussian Processes using Grid-Structured Eigenfunction (GRIEF) kernels.

# Installation & Setup
Only python 2 is supported.
An up-to-date Anaconda distribution is recommended in addition to the following non-standard dependencies (using `pip`):
```
pip install GPy==1.6.1 # v1.6.1 tested but other versions should also work
pip install tabulate # this is a lightweight package used for printing
```
Also required is the forked version of the `py-mcmc` library:
```
git clone https://github.com/treforevans/py-mcmc.git
cd py-mcmc
python setup.py install
```

# Tutorials & Examples
* [Type-II inference with GRIEF kernels:](./tutorials/Type-II%20example%20with%20GRIEF%20kernel.ipynb) This example demonstrates the type-II training and inference procedure with GP-GRIEF.
* [Type-I inference with GRIEF kernels:](./tutorials/Type-I%20example%20with%20GRIEF%20kernel.ipynb) This example demonstrates how to perform fully Bayesian type-I inference with a runtime that is independent of the training dataset size.

# Citation
The underlying algorithms are based on the following 2018 ICML paper (found [here](https://arxiv.org/abs/1807.02125)):

```
@InProceedings{evans_gp-grief,
  title={Scalable {G}aussian Processes with Grid-Structured Eigenfunctions ({GP}-{GRIEF})},
  author={Evans, Trefor W and Nair, Prasanth B},
  booktitle={International Conference on Machine Learning},
  pages={1416--1425},
  year={2018}
}
```

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