> 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
 > 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.
 Konrad Hinsen                            | E-Mail: hinsen (- at -)
 Centre de Biophysique Moleculaire (CNRS) | Tel.: +33-
 Rue Charles Sadron                       | Fax:  +33-
 45071 Orleans Cedex 2                    | Deutsch/Esperanto/English/
 France                                   | Nederlands/Francais