Reversible Jump SMC

Reversible Jump Markov Chain Monte Carlo (RJMCMC) methods are commonly used to estimate models involving discrete parameters, but are slow to converge, with high rejection rates for proposals. We embed RJMCMC proposals into a Sequential Monte Carlo (SMC) sampler, taking advantage of the inherent parallelism of SMC to decrease overall computation time. Additionally, we use the information encoded in the weights of particles in the SMC sampler to inform the probability of particular discrete proposals at each step. We demonstrate that these approaches offer comparable performance and improvements in computation time over conventional RJMCMC in the setting of estimating a finite mixture model with an unknown number of components.