# Re: CCL:Why WHAM?

> I've read Kumar's paper [1] on the WHAM method for combining multiple
> histograms, but it's still not clear to me what's exactly the
> advantage of this method over the original umbrella sampling method
> proposed by Torrie and Valleau? In Chandler's textbook [2] (for
The two main advantages are
1) WHAM can easily be extended to multiple dimension PMFs.
2) WHAM uses the available data more efficiently: in the overlap
regions between the windows all the data is effectively combined
in order to obtain the final PMF.
For a more detailed explanation, see
B. Roux, The Calculation of the Potential of Mean Force using
Computer Simulations, Comp. Phys. Comm. 91, 275 (1995)
> smooth curve (p. 172). This may be a bit crude compared to WHAM, but
> at least it is completely transparent. It is true that this "naive
The theory behind WHAM may be more involved, but it's no less
transparent.
> suffice. My concern is that, in such relatively simple cases, the
> additional complexity of WHAM may not be justified by its advantages.
Which complexity? Sure, the justification of WHAM is more complicated,
but a practical implementation is very simple. I can offer a general
WHAM implementation for any number of variables which consists of
a mere 80 lines of Python code. Mail me if you are interested.
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