Update README

Author: sdatkinson

README.md

@@ -1,2 +1,29 @@
-# structured-gpflow
-Gaussian process models with structured inputs based on GPflow
+# structured-gpflow #
+
+Implements a variety of Gaussian process models exploiting the "structured" 
+assumption that one has inputs that ae formed as a Cartesian product as well 
+as a kernel that is separable so that one may decompose the associated kernel 
+matrices as Kronecker products for a representation that is computationally 
+efficient in terms of both time and memory.  
+The models are built on top of [GPflow](https://github.com/GPflow/GPflow), and 
+the computational backend is [TensorFlow](https://www.tensorflow.org).
+
+## Installation ##
+
+First, install GPflow.
+Note: this repo is designed to work with [this fork](https://github.com/sdatkinson/GPflow).
+Next, simply `python setup.py install` as usual.
+
+## Models ##
+
+* SGPR: Structured GP for regression
+* SGPLVM: Structured Bayesian Gaussian process latent variable model
+* SWGP: Structured Bayesian warped Gaussian processes
+
+See [[Atkinson and Zabaras, 2018]](https://arxiv.org/abs/1805.08665) for more 
+information.
+
+## Questions ##
+
+Contact [Steven Atkinson](mailto:steven@atkinson.mn) or 
+[Nicholas Zabaras](mailto:nzabaras@nd.edu) with questions or comments.