Papers
arxiv:1811.08337
Black-Box Autoregressive Density Estimation for State-Space Models
Published on Nov 20, 2018
Authors:
Abstract
A fast approach using deep learning and variational inference for approximate Bayesian inference in state-space models is provided.
AI-generated summary
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.
Get this paper in your agent:
hf papers read 1811.08337 Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash Models citing this paper 0
No model linking this paper
Cite arxiv.org/abs/1811.08337 in a model README.md to link it from this page.
Datasets citing this paper 0
No dataset linking this paper
Cite arxiv.org/abs/1811.08337 in a dataset README.md to link it from this page.
Spaces citing this paper 0
No Space linking this paper
Cite arxiv.org/abs/1811.08337 in a Space README.md to link it from this page.
Collections including this paper 0
No Collection including this paper
Add this paper to a collection to link it from this page.