Deep Kalman Filters

Understand

Kalman Filters are one of the most influential models of time-varying phenomena.

• They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption in a variety of disciplines.
• Motivated by recent variational methods for learning deep generative models, we introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters.
• Of particular interest is the use of temporal generative models for counterfactual inference.