  Boltzmann sampling based on Metropolis algorithm has been extensively used
for simulating a canonical ensemble. An estimate of a mechanical property, like
energy, of an equilibrium system, can be made by averaging over a large number
microstates generated by Boltzmann Monte Carlo methods. However, a thermal
property like entropy is not easily accessible to these methods. The reason is
simple. We can assign a numerical value for energy to each microstate. But we
can not assign a numerical value for entropy, to a microstate. Entropy is not a
property associated with any single microstate.It is a collective property of
allthe microstates. Toward calculating entropy and other thermal properties, a
non-Boltzmann Monte Carlo technique called Umbrella sampling was proposed in
the mid-seventies (of the last century). Umbrella sampling has since undergone
several metamorphoses and we have now, multi-canonical Monte Carlo, entropic
sampling, flat histogram methods, Wang-Landau algorithm {\it etc.} This class
of methods generates non-Boltzmann ensembles which are un-physical. However,
physical quantities can be calculated by un-weighting of the microstates of the
entropic ensemble, followed by re-weighting to the desired physical ensemble.In
this talk I shall tellyou of a few non-Boltzmann Monte Carlo methods with
emphasis on recent developments.
