ON NON-LINEAR MARKOV CHAIN MONTE CARLO VIA SELF-INTERACTING APPROXIMATIONS (original) (raw)

Let P(E) be the space of probability measures on a measurable space (E, E). In this paper we introduce a class of non-linear Markov Chain Monte Carlo (MCMC) methods for simulating from a probability measure π ∈ P(E). Non-linear Markov kernels (e.g. Del ; Del ) K : P(E) × E → P(E) can be constructed to admit π as an invariant distribution and have superior mixing properties to ordinary (linear) MCMC kernels. However, such non-linear kernels cannot be simulated exactly, so, in the spirit of particle approximations of Feynman-Kac formulae (Del Moral 2004), we construct approximations of the non-linear kernels via Self-Interacting Markov Chains (Del Moral & Miclo 2004) (SIMC). We present several non-linear kernels and demonstrate that, under some conditions, the associated self-interacting approximations exhibit a strong law of large numbers; our proof technique is via the Poisson equation and Foster-Lyapunov conditions. We investigate the performance of our approximations with some simulations, combining the methodology with population-based Markov chain Monte Carlo (e.g. Jasra et al. ). We also provide a comparison of our methods with sequential Monte Carlo samplers when applied to a continuous-time stochastic volatility model.