Black-Box Autoregressive Density Estimation for State-Space Models

11/20/2018

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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.

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Black-Box Autoregressive Density Estimation for State-Space Models

Scalable approximate inference for state space models with normalising flows

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Black-Box Autoregressive Density Estimation for State-Space Models

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Scalable approximate inference for state space models with normalising flows

Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs

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