标题： 摊销变维变分推断 显示英文标题

标题： Amortized variational transdimensional inference

摘要： 基于流的模型与随机变分推理(SVI)的结合，已将基于优化的贝叶斯推断应用扩展到高度复杂的问题。 然而，尽管多模型贝叶斯推断在跨维度联合模型和参数空间上具有重要意义，基于流的SVI仅限于在固定维度参数空间上定义的问题。 我们引入了CoSMIC归一化流(COntextually-Specified Masking for Identity-mapped Components)，这是对神经自回归条件归一化流架构的扩展，使得可以使用单一的摊销变分密度对跨维度(多模型)条件目标分布进行推断。 我们提出了一种结合的随机变分跨维度推断(VTI)方法，利用贝叶斯优化和蒙特卡洛梯度估计的思想来训练CoSMIC流。 数值实验显示VTI在可扩展到高基数模型空间的挑战性问题上的性能。 显示英文摘要

摘要： The expressiveness of flow-based models combined with stochastic variational inference (SVI) has expanded the application of optimization-based Bayesian inference to highly complex problems. However, despite the importance of multi-model Bayesian inference, defined over a transdimensional joint model and parameter space, flow-based SVI has been limited to problems defined over a fixed-dimensional parameter space. We introduce CoSMIC normalizing flows (COntextually-Specified Masking for Identity-mapped Components), an extension to neural autoregressive conditional normalizing flow architectures that enables use of a single amortized variational density for inference over a transdimensional (multi-model) conditional target distribution. We propose a combined stochastic variational transdimensional inference (VTI) approach to training CoSMIC flows using ideas from Bayesian optimization and Monte Carlo gradient estimation. Numerical experiments show the performance of VTI on challenging problems that scale to high-cardinality model spaces.