Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference
利用摊销式模拟推断快速估计贝叶斯状态空间模型
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…