i=1 IXi(x)∫

Abstract In this short note, we show how the parallel adaptive Wang-Landau (PAWL) algorithm of Bornn et al. (2013) can be used to automate and improve simulated tempering algorithms. While Wang-Landau and other stochastic approxi-mation methods have frequently been applied within the simulated tempering frame-work, this note demonstrates through a simple example the additional improvements brought about by parallelization, adaptive proposals and automated bin splitting. 1 A Parallel Adaptive Wang-Landau Algorithm The central idea underlying Wang-Landau ([6]) and related algorithms is that instead of generating samples from a target density pi, it is sometimes more efficient to instead sample a strategically biased density p̃i. In the case of Wang-Landau, the goal is to sample p̃i(x) = pi(x) ×