2011/05/12 by Alexander W. Blocker, Edoardo M. Airoldi, Blocker, Alexander W. +1
Computer Science · Mathematics · Medicine · Neuroscience · #Advanced MRI Techniques and Applications #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Heart Rate Variability and Autonomic Control #Methodology (stat.ME) #Social and Information Networks (cs.SI) #cs.SI #stat.ME
paper · pdf · doi:10.48550/arxiv.1105.2526
10 pages, 11 page supplement; updated with minor edits; accepted into UAI 2011
openalex publication_date 2011/05/12 · arxiv created 2011/06/10 · arxiv updated 2011/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
In many applications we are interested in making inference on latent time series from indirect measurements, which are often low-dimensional projections resulting from mixing or aggregation. Positron emission tomography, super-resolution, and network traffic monitoring are some examples. Inference in such settings requires solving a sequence of ill-posed inverse problems, yt= A xt, where the projection mechanism provides information on A. We consider problems in which A specifies mixing on a graph of times series that are bursty and sparse. We develop a multilevel state-space model for mixing times series and an efficient approach to inference. A simple model is used to calibrate regularization parameters that lead to efficient inference in the multilevel state-space model. We apply this method to the problem of estimating point-to-point traffic flows on a network from aggregate measurements. Our solution outperforms existing methods for this problem, and our two-stage approach suggests an efficient inference strategy for multilevel models of dependent time series.