Black-Box Autoregressive Density Estimation for State-Space Models
arXiv:1811.08337v23 citations
Originality Synthesis-oriented
AI Analysis
This work addresses the need for efficient inference in state-space models, which are widely used in fields like engineering and epidemiology, but it appears incremental as it builds on existing deep learning and variational methods.
The paper tackles the problem of approximate Bayesian inference in state-space models by introducing a fast approach using deep learning and variational inference.
State-space models (SSMs) provide a flexible framework for modelling time-series data. Consequently, SSMs are ubiquitously applied in areas such as engineering, econometrics and epidemiology. In this paper we provide a fast approach for approximate Bayesian inference in SSMs using the tools of deep learning and variational inference.