See [https://github.com/mschauer/ZigZagBoomerang.jl/tree/master/scripts].
using ZigZagBoomerang
# negative log-density with respect to Lebesgue
ϕ(x) = cos(2pi*x) + x^2/2 # not needed
# gradient of ϕ(x)
∇ϕ(x) = -2*pi*sin(2*π*x) + x
x0, θ0 = randn(), 1.0
T = 100.0
c = 2π # parameter for the upper bound of the Poisson rate, will error if too small
# ZigZag
out1 = ZigZagBoomerang.pdmp(∇ϕ, x0, θ0, T, c, ZigZag())
# Boomerang with refreshment rate 0.5
out2 = ZigZagBoomerang.pdmp(∇ϕ, x0, θ0, T, c, Boomerang(0.5))- Joris Bierkens, Paul Fearnhead, Gareth Roberts: The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data. The Annals of Statistics, 2019, 47. Vol., Nr. 3, pp. 1288-1320. [https://arxiv.org/abs/1607.03188].
- Joris Bierkens, Sebastiano Grazzi, Kengo Kamatani and Gareth Robers: The Boomerang Sampler. ICML 2020. [https://arxiv.org/abs/2006.13777].
- Joris Bierkens, Sebastiano Grazzi, Frank van der Meulen, Moritz Schauer: A piecewise deterministic Monte Carlo method for diffusion bridges. 2020. [https://arxiv.org/abs/2001.05889].
- [https://github.com/jbierkens/ICML-boomerang/] (code accompanying the paper "The Boomerang Sampler")