Black-Box Autoregressive Density Estimation for State-Space Models

Abstract

State-space models (SSMs) provide a flexible framework for modelling time-series data. Consequently, SSMs are ubiquitously applied in areas such as engineering, econometrics and epidemiology. In this paper we provide a fast approach for approximate Bayesian inference in SSMs using the tools of deep learning and variational inference.

arXiv commentV2

References in corpus (5)

• Auto-Encoding Variational Bayes
• WaveNet: A Generative Model for Raw Audio
• MADE: Masked Autoencoder for Distribution Estimation
• Black box variational inference for state space models
• Auto-Encoding Sequential Monte Carlo

Cited by in corpus (1)

• Black-Box Inference for Non-Linear Latent Force Models