论文标题
有效的贝叶斯通用线性模型，具有时变系数：R中的Walker包装
Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R
论文作者
论文摘要
R包Walker将标准的贝叶斯通用线性模型扩展到了解释变量的效果可能会随着时间而变化的情况。例如，这允许建模干预措施的影响，例如税收政策的变化，这些变化会随着时间的流逝而逐渐增加其效果。马尔可夫链蒙特卡洛算法为贝叶斯推理提供动力的推断是基于斯坦软件提供的哈密顿蒙特卡洛，使用该模型的状态空间表示，以在回归系数上边缘化有效的低维度采样。
The R package walker extends standard Bayesian general linear models to the case where the effects of the explanatory variables can vary in time. This allows, for example, to model the effects of interventions such as changes in tax policy which gradually increases their effect over time. The Markov chain Monte Carlo algorithms powering the Bayesian inference are based on Hamiltonian Monte Carlo provided by Stan software, using a state space representation of the model to marginalise over the regression coefficients for efficient low-dimensional sampling.
