  Sequential Monte Carlo (SMC) samplers form an attractive alternative to MCMC
for Bayesian computation. However, their performance depends strongly on the
Markov kernels used to rejuvenate particles. We discuss how to calibrate
automatically (using the current particles) Hamiltonian Monte Carlo kernels
within SMC. To do so, we build upon the adaptive SMC approach of Fearnhead and
Taylor (2013), and we also suggest alternative methods. We illustrate the
advantages of using HMC kernels within an SMC sampler via an extensive
numerical study.
