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BLOCK: A Bayesian block method to analyze structure in photon counting data
Jeffrey D. Scargle
NASA Ames Research Center
Abstract:
Bayesian Blocks is a new time-domain algorithm for
detecting localized structures (bursts), revealing pulse shapes,
and generally characterizing intensity variations.
The input is raw counting data, in any of three forms:
time-tagged photon events, binned counts,
or time-to-spill data. The output is the most probable
segmentation of the observation into time intervals during which
the photon arrival rate is perceptibly constant -- i.e. has no
statistically significant variations. The idea is not that the
source is deemed to have this discontinuous, piecewise constant form,
rather that such an approximate and generic model is often useful.
The analysis is based on Bayesian statistics.
Subject headings: methods: data analysis -- methods: numerical -
methods: statistical -- gamma rays: bursts
Latest Version: 1999 September 24
Archived: 1999 September 27
Paper:
Scargle, J. D., 1998ApJ...504..405S
Preprints:
http://xxx.lanl.gov/abs/astro-ph/9711233,
http://xxx.lanl.gov/abs/astro-ph/9712016
Language:
MatLab
External Explanatory Page:
http://ccf.arc.nasa.gov/~scargle/papers.html
Source Codes:
main.m,
make_segments.m,
find_change.m,
block_half.m,
block_pulses.m,
show_blocks.m,
hist_fast.m