Merge pull request #19 from UM-Bridge/marlenaweidenauer-patch-1

Update README.md

clients/README.md

@@ -335,4 +335,52 @@ idata = tda.to_inference_data(my_chains, burnin=1000)
 az.summary(idata)
 ```
 
-[Full example sources here.](https://github.com/UM-Bridge/umbridge/blob/main/clients/python/tinyDA-client.ipynb)
+[Full example sources here.](https://github.com/UM-Bridge/umbridge/blob/main/clients/python/tinyDA-client.ipynb)
+
+## emcee client
+
+emcee is a python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC). It supports UM-Bridge models and can be installed from pip just like UM-Bridge.
+
+```
+pip install umbridge emcee
+```
+
+A basic emcee example using an UM-Bridge model is shown below.
+
+```
+import emcee
+from umbridge.emcee import UmbridgeLogProb
+import arviz as az
+import argparse
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+if __name__ == "__main__":
+
+    # Read URL from command line argument
+    parser = argparse.ArgumentParser(description='Minimal emcee sampler demo.')
+    parser.add_argument('url', metavar='url', type=str,
+                        help='the ULR on which the model is running, for example http://localhost:4242')
+    args = parser.parse_args()
+    print(f'Connecting to host URL {args.url}')
+
+    log_prob = UmbridgeLogProb(args.url, 'posterior')
+
+    nwalkers = 32
+    sampler = emcee.EnsembleSampler(nwalkers, log_prob.ndim, log_prob)
+
+    # run sampler
+    p0 = np.random.rand(nwalkers, log_prob.ndim)
+    state = sampler.run_mcmc(p0, 100)
+
+    # plot results
+    inference_data = az.from_emcee(sampler)
+    az.plot_pair(inference_data)
+    plt.tight_layout()
+    plt.savefig('emcee_inference.png')
+
+    print(az.summary(inference_data, round_to=2))
+```
+
+[Full example sources here.](https://github.com/UM-Bridge/umbridge/blob/main/clients/python/emcee-client.py)