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Optimal Estimate of Monotonic Trend with Sparse Jumps
2007
American Control Conference (ACC)
This paper discusses a problem for recovering an underlying trend from noisy data. The key assumption is that the trend is monotonic, e.g., reflects accumulation of irreversible system deterioration. The trend is obtained as a maximum a posteriori probability estimate. The overall problem setup is related to α-β filter and Hodrick-Prescott filter. The main difference is that instead of a Gaussian process noise, a onesided exponentially distributed noise is assumed. The batch estimate is a
doi:10.1109/acc.2007.4282395
dblp:conf/acc/Gorinevsky07
fatcat:nq22mxsqtvc2xn74t4ocw2saxu