论文标题
使用基于摊销模拟的推理对贝叶斯状态空间模型的快速估算
Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference
论文作者
论文摘要
本文提出了一种快速算法，用于估计贝叶斯状态空间模型的隐藏状态。该算法是基于摊销模拟的推理算法的一种变体，其中在第一阶段生成了大量的人工数据集，然后对灵活的模型进行了训练以预测感兴趣的变量。与前面提出的相反，本文中描述的程序可以通过仅专注于边缘后验分布的某些特征并引入电感偏见来训练隐藏状态的估计量。使用随机波动率模型的示例，非线性动态随机通用平衡模型和季节性调整程序随季节性中断表明，该算法具有足够的精度用于实际使用。此外，经过预处理（需要几个小时），找到任何数据集的后验分布所需的时间是百分之一到十分之一。
This paper presents a fast algorithm for estimating hidden states of Bayesian state space models. The algorithm is a variation of amortized simulation-based inference algorithms, where a large number of artificial datasets are generated at the first stage, and then a flexible model is trained to predict the variables of interest. In contrast to those proposed earlier, the procedure described in this paper makes it possible to train estimators for hidden states by concentrating only on certain characteristics of the marginal posterior distributions and introducing inductive bias. Illustrations using the examples of the stochastic volatility model, nonlinear dynamic stochastic general equilibrium model, and seasonal adjustment procedure with breaks in seasonality show that the algorithm has sufficient accuracy for practical use. Moreover, after pretraining, which takes several hours, finding the posterior distribution for any dataset takes from hundredths to tenths of a second.
