Enhancing Score-Based Sampling Methods with Ensembles

We We introduce ensembles within score-based sampling build on these ideas and introduce ensemble strategies that methods to develop gradient-free approximate leverage the collective dynamics of a particle ensemble to sampling techniques that leverage the collective approximately compute the score function within a reverse dynamics of particle ensembles to compute diffusion process. Compared to the aforementioned Föllmer approximate reverse diffusion drifts. We sampler, a goal is to reduce evaluations of the probability introduce the underlying methodology, emphasizing distribution that one wants to sample from. In the setting its relationship with generative diffusion of Bayesian inverse problems, this helps to reduce forward models and the previously introduced Föllmer model evaluations, thereby offering an efficient sampling sampler. We demonstrate the efficacy of ensemble technique. More concretely, we introduce an importance strategies through various examples, ranging sampling Monte Carlo estimator for the score function of a from low-to medium-dimensionality sampling forward diffusion process in order to sample from a probability problems, including multi-modal and highly non-distribution using the associated reverse diffusion Gaussian probability distributions, and provide process. Because there is some flexibility in the choice of comparisons to traditional methods like NUTS.