Subject: Re: CCL:Why WHAM?

From: [email-masked]
Date: Fri, 28 Jan 2000 13:45:33 -0500 (EST)

Date: Thu, 27 Jan 2000 17:35:55 +0100
   From: [email-masked]
   Subject: Re: CCL:Why WHAM?

   The theory behind WHAM may be more involved, but it's no less
   transparent.

There are a couple of items in WHAM that are less than transparent, I
think.  One is the assumption that the single integrated correlation
time that one can compute from the simulation's energy data is a good
enough approximation for the integrated correlation time for the
occupancy of each bin (which, in principle, depends on the bin's
coordinates); it is the latter that we need to estimate the error that
we want to minimize.  Given that the minimization of this error is the
crux of the method, it seems that the published applications of WHAM
are extremely cavalier about determining this error.  Also, I've not
seen a justification for assuming that the bin occupancy has a Poisson
distribution, or at any rate, that its variance equals its mean (this
assumption is implicit in the formula Kumar gives for \tau).

None of these concerns arise with the more naive approach to combining
the histograms, because this method does not depend crucially on a
good estimate of the error in the histogram.

Thanks for the reference by Roux, and for the Python code.

KJ