docs and readme udpate

README.md

@@ -6,6 +6,8 @@ SGMCMCJax is a lightweight library of stochastic gradient Markov chain Monte Car
 
 The target audience for this library is researchers and practitioners: simply plug in your JAX model and easily obtain samples.
 
+Note that this library is still in its early stages so expect the API to change a bit.
+
 ## Example usage
 
 We show the basic usage with the following example of estimating the mean of a D-dimensional Gaussian from data using a Gaussian prior.

docs/all_samplers.rst

@@ -5,7 +5,9 @@ SGMCMC samplers
 Samplers:
 ---------
 
-There are several SGMCMC samplers available. Each comes with its own set of tradeoffs. Here we list them and very briefly describe the pros and cons of each.
+There are several SGMCMC samplers available. Each comes with its own set of tradeoffs. Here we list them and very briefly describe the pros and cons of each. You can see them in action in `this notebook`_.
+
+.. _this notebook: nbs/sampler.ipynb
 
 SGLD:
 ^^^^^
@@ -79,7 +81,7 @@ pSGLD:
 
 `Preconditioned SGLD`_: SGLD with an adaptive (diagonal) preconditioner; this is essentially RMSProp merged with SGLD. Note that we set :math:`\Gamma(\theta)=0` as recommended in the paper.
 
-.. _Preconditioned SGLD: SVRG-Langevin
+.. _Preconditioned SGLD: https://arxiv.org/abs/1512.07666
 
 **Pros:** The preconditioner can help with poorly scaled posteriors