Liesel is a Python framework for Bayesian model building and posterior computation with dedicated support for generalized additive regression models that is designed to reduce friction in methodological work. The framework consists of three components. The first component, Liesel-Model, represents models as directed acyclic graphs and supports interactive model construction, modification, visualization, prediction, and prior or posterior predictive simulation. The second component, Liesel-Goose, provides a modular MCMC framework based on reusable kernels and supports blocked componentwise sampling as well as user-defined Gibbs and Metropolis--Hastings updates, while leveraging JAX for automatic differentiation, just-in-time compilation, and hardware acceleration. The third component, Liesel-GAM, supplies high-level building blocks for generalized additive regression models and provides functionality for formula-based model specification, summaries, diagnostics, and effect visualization. Together, these parts make Liesel an effective tool for the rapid development, testing, and application of Bayesian models and MCMC algorithms. Liesel's modular architecture allows users to extend the software to models and inference algorithms beyond generalized additive models and MCMC, offering considerable flexibility for a wide spectrum of statistical research.

翻译：Liesel是一个用于贝叶斯模型构建与后验计算的Python框架，其专门支持广义加性回归模型，旨在降低方法论研究中的摩擦。该框架由三个组件构成。第一个组件Liesel-Model将模型表示为有向无环图，支持交互式模型构建、修改、可视化、预测以及先验或后验预测模拟。第二个组件Liesel-Goose提供一个基于可复用核的模块化MCMC框架，支持分块分量采样以及用户自定义的吉布斯采样和Metropolis-Hastings更新，同时利用JAX实现自动微分、即时编译和硬件加速。第三个组件Liesel-GAM为广义加性回归模型提供高级构建模块，并提供基于公式的模型规范、汇总、诊断和效应可视化功能。这些组件共同使Liesel成为快速开发、测试和应用贝叶斯模型及MCMC算法的有效工具。Liesel的模块化架构允许用户将软件扩展到广义加性模型和MCMC之外的模型与推断算法，为广泛的统计研究提供了相当大的灵活性。