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  This function computes an upper limit for the strength of a signal
  embedded in a sequence of noisy measurements.




  signal: The SUM of n independent measurements.
  noise: The per-measurement noise standard deviation. Currently, the
        noise is assumed to be normally distributed.
      n: The number of measurements that went into the sum
    mu: The per-measurement background level.

Keyword Parameters

  confidence: The posterior probability that the true flux is less
              than the reported upper limit. The default is .998650
              (3-sigma, one tailed).
  plot: If set, plot the posterior distribution and upper limit.


  An upper limit for any INTEGRATED source flux (that is, summed up
  over all measurements) embedded in the signal.


  Signal is assumed to be the sum of n independent measurements from
  a process given by y = mu + source_flux + eps, where eps is
  gaussian noise with mean zero and variance sigma. The procedure
  employs a Bayesian approach to find an upper limit for source_flux
  * n. A prior enforces that source_flux >= 0. The program returns
  the value f_crit = source_flux_crit * n such that the posterior
  probability that f_true < f_crit is equal to confidence


  Add in Poisson (and other) noise models

Modification History

  Sep 2009: Written by Chris Beaumont
  Sep 14 2009: Added input parameter checking. cnb.

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