Schrödinger-Föllmer Sampler

Sampling from probability distributions is a critical problem in statistics and machine learning, particularly in Bayesian inference, where direct integration over the posterior distribution is often infeasible, making sampling from the posterior essential for inference. This paper introduces the Schrödinger-Föllmer sampler (SFS), a novel approach for sampling from potentially unnormalized distributions. The SFS leverages the Schrödinger-Föllmer diffusion process on the unit interval, incorporating a time-dependent drift term that evolves the distribution from a degenerate form at time zero to the target distribution at time one. Unlike existing Markov chain Monte Carlo methods that rely on ergodicity, SFS operates independently of ergodicity. Computationally, SFS is straightforward to implement using the Euler-Maruyama discretization. In our theoretical analysis, we derive non-asymptotic error bounds for the SFS sampling distribution in the Wasserstein distance, subject to reasonable conditions. Numerical experiments demonstrate that SFS generates higher-quality samples than several established methods.