diff --git a/README.md b/README.md
index 5b59aff4..98757f98 100644
--- a/README.md
+++ b/README.md
@@ -1,15 +1,10 @@
-# A No-U-Turn Sampler (NUTS) Implementation in Julia
+# Efficient Hamiltonian Monte Carlo implementation in Julia
 
 **The code from this repository is used to implement NUTS in [Turing.jl](https://github.com/yebai/Turing.jl). Try it out when it's available!**
 
-This package implements the No-U-Turn Sampler (NUTS) described in Algorithm 2, 3 and 6 from ([Hoffman & Gelman, 2011][1]).
 
 ## Usage
 
-1. Clone this repository in a location (call `PATH`)
-2. In order to use this package, add `include("$PATH/nuts.jl"); using NUTSJulia;`in the top of you file
-3. Generate your own initial parameter & log-probaiblity function and pass it with the corresponding setting parameters to the sampler (see API below)
-
 See `example.jl` for an example of sampling from a mixture of Gaussians.
 
 ## API
@@ -37,6 +32,17 @@ NUTS(θ0, 0.65, x -> log(f(x)), 2000, 200)
 
 ## Reference
 
-[arXiv:1111.4246][1]: Hoffman, M. D., & Gelman, A. (2011, November 18). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. arXiv.org.
+1. Neal, R. M. (2011). MCMC using Hamiltonian dynamics. Handbook of Markov chain Monte Carlo, 2(11), 2. ([pdf](https://arxiv.org/pdf/1206.1901))
+
+2. Betancourt, M. (2017). A Conceptual Introduction to Hamiltonian Monte Carlo. [arXiv preprint arXiv:1701.02434](https://arxiv.org/abs/1701.02434).
+
+3. Girolami, M., & Calderhead, B. (2011). Riemann manifold Langevin and Hamiltonian Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(2), 123-214. ([link](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1467-9868.2010.00765.x))
+
+4. Betancourt, M. J., Byrne, S., & Girolami, M. (2014). Optimizing the integrator step size for Hamiltonian Monte Carlo. [arXiv preprint arXiv:1411.6669](https://arxiv.org/pdf/1411.6669).
+
+5. Betancourt, M. (2016). Identifying the optimal integration time in Hamiltonian Monte Carlo. [arXiv preprint arXiv:1601.00225](https://arxiv.org/abs/1601.00225).
+
+6. Hoffman, M. D., & Gelman, A. (2014). The No-U-Turn Sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1), 1593-1623. ([link][1])
+
 
 [1]: http://arxiv.org/abs/1111.4246