Review for NeurIPS paper: Deep Rao-Blackwellised Particle Filters for Time Series Forecasting

Strengths: Soundness: The model formulation appears to be mathematically sound. As with several previous works, the authors utilize linear-Gaussian distributions for dynamics, which have the benefit of permitting exact computation of expectations, e.g. The authors propose two main improvements over related models: 1) the use of recurrent switch transitions through Gaussian switch variables, and 2) non-linear emission models through the use of an additional (auxiliary) latent variable, z. They train this model with a sequential Monte Carlo objective utilized in previous works. This paper builds off of many of the theoretical developments of previous works, adding a couple of useful techniques.