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May, 2018
可扩展贝叶斯状态空间模型的变分推断与SMC取样算法
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers
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Marcel Hirt, Petros Dellaportas
TL;DR
本文提出了一种可扩展的近似贝叶斯推断方法，在泛型状态空间模型中，相较于粒子MCMC提供了动态潜在状态和模型静态参数的完全贝叶斯推断，从而在多元随机波动模型和自激兴奋点过程模型中实现了可扩展的推断。
Abstract
We present a scalable approach to performing approximate fully
bayesian inference
in generic
state space models
. The proposed method is an alternative to
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