The neural moving average model for scalable variational inference of state space models
The neural moving average model for scalable variational inference of state space models
复制标题
用于状态空间模型的可扩展变分推理的神经移动平均模型
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Isaac Matthews
中科院分区：
文献类型：
--
作者：
Tom Ryder;D. Prangle;A. Golightly;Isaac Matthews
变分推理通过利用小批量训练在将近似贝叶斯推理扩展到大数据方面取得了巨大成功。然而，到目前为止，这种策略最适用于独立数据的模型。我们提出了一个扩展的时间序列数据的状态空间模型的基础上一种新的生成模型的潜在的时间状态：神经移动平均模型。这允许在不从整个分布中提取的情况下对子序列进行采样，从而使训练迭代能够以低计算成本使用小批量的时间序列。我们说明了我们的方法上的自回归，Lotka-Volterra，FitzHugh-Nagumo和随机波动率模型，在短时间内实现准确的参数估计。
Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training. To date, however, this strategy has been most applicable to models of independent data. We propose an extension to state space models of time series data based on a novel generative model for latent temporal states: the neural moving average model. This permits a subsequence to be sampled without drawing from the entire distribution, enabling training iterations to use mini-batches of the time series at low computational cost. We illustrate our method on autoregressive, Lotka-Volterra, FitzHugh-Nagumo and stochastic volatility models, achieving accurate parameter estimation in a short time.