发表机构
Eindhoven University of Technology; Donders Institute for Brain, Cognition and Behaviour; Radboud University（埃因霍温理工大学; 唐德斯大脑、认知与行为研究所; 拉德堡德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对RJMCMC模型间提议设计困难及GPU并行受限问题，提出锚定可逆跳跃序贯蒙特卡洛，结合SMC并行探索模型空间，利用粒子构建核密度近似生成跨维度提议，实现高效且可扩展的贝叶斯模型比较。
AI 中文摘要
可逆跳跃马尔可夫链蒙特卡洛（RJMCMC）是贝叶斯模型比较的一个原则性框架，但其实际应用常因难以设计能到达高后验概率区域的模型间提议而受限。此外，其固有的序列特性限制了现代GPU架构的高效利用。我们通过引入锚定可逆跳跃序贯蒙特卡洛方法同时应对这两个挑战。我们的方法将RJMCMC与序贯蒙特卡洛（SMC）相结合，以并行方式探索模型空间。重要的是，粒子群体使得无需问题特定知识即可实现有效的模型间提议：粒子被用于构建每个模型内目标分布的核密度近似，进而用于生成跨维度提议。数值实验表明，所提方法计算高效，且与最先进的RJMCMC方法相比具有竞争力，展示了其在可扩展贝叶斯模型比较方面的潜力。
英文摘要
Reversible Jump Markov Chain Monte Carlo (RJMCMC) is a principled framework for Bayesian model comparison, but its practical use is often limited due to the difficulty of designing between-model proposals that reach regions of high posterior probability. In addition, its inherently sequential nature limits efficient use of modern GPU architectures. We address both challenges by introducing Anchored Reversible Jump Sequential Monte Carlo. Our approach combines RJMCMC with Sequential Monte Carlo (SMC) to explore the model space in parallel. Importantly, the particle population enables effective between-model proposals without problem-specific knowledge: particles are used to construct kernel density approximations of the target distributions within each model, which are then used to generate trans-dimensional proposals. Numerical experiments show that the proposed method is computationally efficient and competitive with state-of-the-art RJMCMC approaches, demonstrating its potential for scalable Bayesian model comparison.
Comments20 pages, 4 figures
