Particle Smoothing Variational Objectives

Understand

A body of recent work has focused on constructing a variational family of filtered distributions using Sequential Monte Carlo (SMC).

• Inspired by this work, we introduce Particle Smoothing Variational Objectives (SVO), a novel backward simulation technique and smoothed approximate posterior defined through a subsampling process.
• SVO augments support of the proposal and boosts particle diversity.
• Recent literature argues that increasing the number of samples K to obtain tighter variational bounds may hurt the proposal learning, due to a signal-to-noise ratio (SNR) of gradient estimators decreasing at the rate $\mathcal{O}(1/\sqrt{K})$.