基于LibBi的高性能硬件贝叶斯状态空间建模
Bayesian State-Space Modelling on High-Performance Hardware Using LibBi
摘要：
LibBi是一个软件包，用于在现代计算机硬件上进行状态空间建模和贝叶斯推断，包括多核中央处理器（CPU），众核图形处理器（GPU）和此类设备的分布式内存集群。该软件解析特定于领域的语言以进行模型规范，然后针对给定的模型、推理方法和硬件平台优化、生成、编译和运行代码。在介绍软件，这项工作作为一个介绍状态空间模型和专门的方法开发的贝叶斯推断与他们。重点是序贯蒙特卡罗（SMC）方法，如用于状态估计的粒子滤波器，以及用于参数估计的粒子马尔可夫链蒙特卡罗（PMCMC）和SMC ^2方法。所有这些都非常适合当前的计算机硬件。给出了两个例子，一个是人体动脉系统的线性三元件windkessel模型，另一个是非线性Lorenz '96模型。这些都是在规定的建模语言中指定的，LibBi通过使用它们进行推理来演示。实证结果，包括不同的硬件配置的软件的性能比较。
英文摘要：
LibBi is a software package for state-space modelling and Bayesian inference on modern computer hardware, including multi-core central processing units (CPUs), many-core graphics processing units (GPUs) and distributed-memory clusters of such devices. The software parses a domain-specific language for model specification, then optimises, generates, compiles and runs code for the given model, inference method and hardware platform. In presenting the software, this work serves as an introduction to state-space models and the specialised methods developed for Bayesian inference with them. The focus is on sequential Monte Carlo (SMC) methods such as the particle filter for state estimation, and the particle Markov chain Monte Carlo (PMCMC) and SMC^2 methods for parameter estimation. All are well-suited to current computer hardware. Two examples are given and developed throughout, one a linear three-element windkessel model of the human arterial system, the other a nonlinear Lorenz '96 model. These are specified in the prescribed modelling language, and LibBi demonstrated by performing inference with them. Empirical results are presented, including a performance comparison of the software with different hardware configurations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
