Continuous problems are often simpler to solve than discrete problems. This is true in many optimization problems (for instance, linear programming versus integer linear programming). In the case of Markov chain Monte Carlo (MCMC), sampling continuous distributions has some advantages over sampling discrete distributions due to the availability of gradient information in the continuous case. The paper “Continuous Relaxations for Discrete Hamiltonian Monte Carlo” by Yichuan Zhang, Charles Sutton, Amos Storkey, and Zoubin Ghahramani explores the idea of performing inference in the discrete setting by deriving and sampling a related continuous distribution. Here I describe the approach taken in this paper.