@inproceedings{0da902ff549f406b9a6f8bb92241e2f4,
title = "The Neural Moving Average Model for Scalable Variational Inference of State Space Models",
abstract = "Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training. To date, however, this strategy has been most applicable to models of independent data. We propose an extension to state space models of time series data based on a novel generative model for latent temporal states: the neural moving average model. This permits a subsequence to be sampled without drawing from the entire distribution, enabling training iterations to use mini-batches of the time series at low computational cost. We illustrate our method on autoregressive, Lotka-Volterra, FitzHugh-Nagumo and stochastic volatility models, achieving accurate parameter estimation in a short time.",
author = "Thomas Ryder and Dennis Prangle and Andrew Golightly and Isaac Matthews",
year = "2021",
month = jul,
day = "27",
language = "English",
volume = "161",
series = "Proceedings of Machine Learning Research",
publisher = "Proceedings of Machine Learning Research",
pages = "12--22",
booktitle = "Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence",
note = "Conference on Uncertainty in Artificial Intelligence 2021, UAI 2021 ; Conference date: 27-07-2021",
}