  I describe 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 (TTE), binned counts, or time-to-spill (TTS) data. The output is
the most likely segmentation of the observation into time intervals during
which the photon arrival rate is perceptibly constant -- i.e. has a fixed
intensity without statistically significant variations. Since the analysis is
based on Bayesian statistics, I call the resulting structures Bayesian Blocks.
Unlike most, this method does not stipulate time bins -- instead the data
themselves determine a piecewise constant representation. Therefore the
analysis procedure itself does not impose a lower limit to the time scale on
which variability can be detected. Locations, amplitudes, and rise and decay
times of pulses within a time series can be estimated, independent of any
pulse-shape model -- but only if they do not overlap too much, as deconvolution
is not incorporated. The Bayesian Blocks method is demonstrated by analyzing
pulse structure in BATSE $\gamma$-ray data. The MatLab scripts and sample data
can be found on the WWW at: http://george.arc.nasa.gov/~scargle/papers.html
