Merge pull request #8 from mj-will/update-readme

DOC: update readme

README.md

@@ -1,2 +1,76 @@
 # nessai-bilby
-Interface and plugin for using nessai in bilby
+
+Interface and plugin for using `nessai` in `bilby`.
+
+This plugin provides two samplers that can be used in `bilby`:
+
+- `nessai`: the standard nested sampler from `nessai`
+- `inessai`: the importance nested sampler from nessai
+
+
+It also provides a means to use `bilby` likelihoods and priors directly in
+`nessai`, see [using bilby likelihoods in nessai](#using-bilby-likelihoods-in-nessai)
+
+## Installation
+
+The package can be installed using pip
+
+```
+pip install nessai-bilby
+```
+or conda
+
+```
+conda install conda-forge::nessai-bilby
+```
+
+However, we recommend following installing PyTorch manually to ensure the
+correct device support.
+
+**Note:** this plugin requires "bilby>=2.3.0".
+
+
+## Usage
+
+### In bilby
+
+One `nessai-bilby` is installed, both samplers can be used directly in `bilby`
+via the `run_sampler` function. See the bilby documentation for more details
+on how to run different samplers.
+
+
+### Using bilby likelihoods in nessai
+
+`nessai-bilby` also provides two model classes that allow bilby likelihood and
+priors to be used directly with nessai:
+
+- `nessai_bilby.model.BilbyModel`: 
+- `nessai_bilby.model.BilbyModelLikelihoodConstraint`:
+
+
+Either model can be used by creating an instance of the model and running `nessai` as usual:
+
+```python
+from nessai.flowsampler import FlowSampler
+from nessai_bilby.model import BilbyModel
+
+likelihood = ...    # bilby likelihood object
+priors = ... # bilby PriorDict
+
+model = BilbyModel(
+    priors=priors,
+    likelihood=likelihood,
+    use_ratio=True    # Whether to use the log-likelihood ratio
+)
+
+fs = FlowSampler(
+    model,
+    ...,
+)
+
+fs.run()
+```
+
+## Citing
+
+If you use `nessai-bilby`, please cite the `nessai` and `bilby` code bases and the corresponding papers.