Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations
Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations
复制标题
基于连续和点过程观测的时空系统变分估计
DOI：
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发表时间：
2012
影响因子：
5.4
通讯作者：
V. Kadirkamanathan
中科院分区：
文献类型：
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作者：
Andrew Zammit;G. Sanguinetti;V. Kadirkamanathan
时空模型在科学和工程中无处不在，但从离散观测值估计这些模型仍然具有计算挑战性。我们提出了一个实用的新方法来推断时空过程，从连续和离散（点过程）的观察。该方法是基于有限维减少的时空模型，其次是平均场变分近似推理方法。为了迎合点过程的情况下，变分拉普拉斯方法提出了产生易于处理的计算近似变分后验。结果表明，变分贝叶斯是一个可行的和实用的替代统计方法，如期望最大化或马尔可夫链蒙特卡罗。
Spatiotemporal models are ubiquitous in science and engineering, yet estimation in these models from discrete observations remains computationally challenging. We propose a practical novel approach to inference in spatiotemporal processes, both from continuous and from discrete (point-process) observations. The method is based on a finite-dimensional reduction of the spatiotemporal model, followed by a mean field variational approximate inference approach. To cater for the point-process case, a variational-Laplace approach is proposed which yields tractable computations of approximate variational posteriors. Results show that variational Bayes is a viable and practical alternative to statistical methods such as expectation maximization or Markov chain Monte Carlo.