Liesel is a new probabilistic programming framework developed with the aim of supporting research on Bayesian inference based on Markov chain Monte Carlo (MCMC) simulations in general and semi-parametric regression specifications in particular. Its three main components are (i) an R interface (RLiesel) for the configuration of an initial semi-parametric regression model, (ii) a graph-based model building library, where the initial model graph can be manipulated to incorporate new research ideas, and (iii) an MCMC library for designing modular inference algorithms combining multiple types of well-tested and possibly customized MCMC kernels. The graph builder as well as the MCMC library are implemented in Python, relying on JAX as a numerical computing library, and can therefore benefit from the latest machine learning technology such as automatic differentiation, just-in-time (JIT) compilation, and the use of high-performance computing devices such as tensor processing units (TPUs). Liesel provides all required tools for efficient and reliable statistical research on complex models and estimation algorithms. Its modular design allows users to expand the model library and inference algorithms, offering the flexibility and customization options to tailor the software to any specific research needs.

翻译：Liesel是一个新型概率编程框架，旨在支持基于马尔可夫链蒙特卡洛（MCMC）模拟的贝叶斯推断研究，尤其聚焦于半参数回归规范。其三大核心组件包括：(i) 用于配置初始半参数回归模型的R接口（RLiesel），(ii) 基于图的模型构建库（可对初始模型图进行操作以融入新研究思路），以及(iii) 用于设计模块化推断算法的MCMC库（该库可组合多种经过充分测试的MCMC内核及可能的定制化内核）。图构建器与MCMC库均基于Python实现，并依托JAX作为数值计算库，因此可受益于自动微分、即时编译（JIT）以及张量处理单元（TPU）等高性能计算设备等最新机器学习技术。Liesel为复杂模型与估计算法的高效可靠统计研究提供了全部必要工具。其模块化设计允许用户扩展模型库与推断算法，为满足特定研究需求提供灵活的定制化选项。