Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations

@inproceedings{Scargle2013StudiesIA,
  title={Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations},
  author={Jeffrey D. Scargle and Jay Norris and Brad Jackson and James Chiang},
  year={2013}
}
  • Jeffrey D. Scargle, Jay Norris, +1 author James Chiang
  • Published 2013
  • Physics, Mathematics
  • This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it—an improved and generalized version of Bayesian Blocks [Scargle 1998]—that finds the optimal segmentation of the data in… CONTINUE READING

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