2008/01/01 by Peter J. Bickel, Peter Bickel, Bo Li +1 · 2 citations
Computer Science · Mathematics · #Markov Chains and Monte Carlo Methods #Random Matrices and Applications #Target Tracking and Data Fusion in Sensor Networks #math.ST #msc:60G50 #msc:62L12 #msc:86A10 #msc:86A22 #msc:86A32 #msc:93E11 #stat.ME #stat.TH
paper · pdf · doi:10.1214/074921708000000228
published as IMS Collections 2008, Vol. 3, 318-329 · Published in at http://dx.doi.org/10.1214/074921708000000228 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2008/01/01 · arxiv created 2008/05/21 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
<!-- *** Custom HTML *** --> We prove that the maximum of the sample importance weights in a high-dimensional Gaussian particle filter converges to unity unless the ensemble size grows exponentially in the system dimension. Our work is motivated by and parallels the derivations of Bengtsson, Bickel and Li (2007); however, we weaken their assumptions on the eigenvalues of the covariance matrix of the prior distribution and establish rigorously their strong conjecture on when weight collapse occurs. Specifically, we remove the assumption that the nonzero eigenvalues are bounded away from zero, which, although the dimension of the involved vectors grow to infinity, essentially permits the effective system dimension to be bounded. Moreover, with some restrictions on the rate of growth of the maximum eigenvalue, we relax their assumption that the eigenvalues are bounded from above, allowing the system to be dominated by a single mode.