Bouncy particle sampler with infinite exchanging parallel tempering
This work addresses sampling efficiency for multimodal distributions in Bayesian inference, representing an incremental improvement over existing methods.
The authors tackled the challenge of accelerating convergence to multimodal posterior distributions in Bayesian inference by introducing parallel tempering to the bouncy particle sampler and proposing an algorithm with infinite inverse temperature exchange rates, demonstrating its effectiveness in numerical simulations.
Bayesian inference is useful to obtain a predictive distribution with a small generalization error. However, since posterior distributions are rarely evaluated analytically, we employ the variational Bayesian inference or sampling method to approximate posterior distributions. When we obtain samples from a posterior distribution, Hamiltonian Monte Carlo (HMC) has been widely used for the continuous variable part and Markov chain Monte Carlo (MCMC) for the discrete variable part. Another sampling method, the bouncy particle sampler (BPS), has been proposed, which combines uniform linear motion and stochastic reflection to perform sampling. BPS was reported to have the advantage of being easier to set simulation parameters than HMC. To accelerate the convergence to a posterior distribution, we introduced parallel tempering (PT) to BPS, and then proposed an algorithm when the inverse temperature exchange rate is set to infinity. We performed numerical simulations and demonstrated its effectiveness for multimodal distribution.