SIXO: Smoothing Inference with Twisted Objectives

Dieterich Lawson, Allan Raventos, Andrew Warrington, Scott Linderman

Keywords: smoothing, variational, objectives, fivo, sequential Monte Carlo, inference, twisted, time series

TL;DR: We develop a new method for smoothing SMC that uses twisting functions learned with density ratio estimation.

Abstract: Sequential Monte Carlo (SMC) is an inference algorithm for state space models that approximates the posterior by sampling from a sequence of target distributions. The target distributions are often chosen to be the filtering distributions, but these ignore information from future observations, leading to practical and theoretical limitations in inference and model learning. We introduce SIXO, a method that instead learns target distributions that approximate the smoothing distributions, incorporating information from all observations. The key idea is to use density ratio estimation to fit functions that warp the filtering distributions into the smoothing distributions. We then use SMC with these learned targets to define a variational objective for model and proposal learning. SIXO yields provably tighter log marginal lower bounds and offers more accurate posterior inferences and parameter estimates in a variety of domains.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2206.05952/code)