Stochastic Gradient MCMC for State Space Models

arXiv: Machine Learning, Volume abs/1810.09098, 2018.

Cited by: 6|Views35
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Abstract:

State space models (SSMs) are a flexible approach to modeling complex time series. However, inference in SSMs is often computationally prohibitive for long time series. Stochastic gradient MCMC (SGMCMC) is a popular method for scalable Bayesian inference for large independent data. Unfortunately when applied to dependent data, such as in ...More

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