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Black-Box Autoregressive Density Estimation for State-Space Models

Machine Learning 2018-11-22 v2 Machine Learning
Authors: Tom Ryder , Andrew Golighty , A. Stephen McGough , Dennis Prangle
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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.

Keywords

state-space models spatio-temporal modeling bayesian variable selection

Cite

@article{arxiv.1811.08337,
  title  = {Black-Box Autoregressive Density Estimation for State-Space Models},
  author = {Tom Ryder and Andrew Golighty and A. Stephen McGough and Dennis Prangle},
  journal= {arXiv preprint arXiv:1811.08337},
  year   = {2018}
}

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