Large-Flip Importance Sampling
This addresses a specific issue in MCMC sampling for researchers in computational statistics, but appears incremental as it builds directly on the N-Fold Way.
The paper tackles the problem of the N-Fold Way MCMC sampler getting trapped in cycles by proposing a new Monte Carlo algorithm that modifies the sampling process and corrects bias with importance sampling, resulting in a method for complex discrete distributions.
We propose a new Monte Carlo algorithm for complex discrete distributions. The algorithm is motivated by the N-Fold Way, which is an ingenious event-driven MCMC sampler that avoids rejection moves at any specific state. The N-Fold Way can however get "trapped" in cycles. We surmount this problem by modifying the sampling process. This correction does introduce bias, but the bias is subsequently corrected with a carefully engineered importance sampler.