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                Examples for event biasing: B01, B02 and B03
                --------------------------------------------
 
 B01, B02 and B03 applications demonstrate the usage of different variance
 reduction techniques supported in Geant4, or possible from the user
 applications.
 
 General remark to variance reduction
 ------------------------------------
 The tools provided for importance sampling (or geometrical splitting and
 Russian roulette) and for the weight window technique require the user to 
 have a good understanding of the physics in the problem. This is because 
 the user has to decide which particle types have to be biased, define the 
 cells (physical volumes, replicas) and assign importances or weight 
 windows to that cells. If this is not done properly it can not be 
 expected that the results describe a real experiment. The examples given 
 here only demonstrate how to use the tools technically. They don't intend 
 to produce physical correct results.
 
 General remark to scoring
 -------------------------
 Scoring is carried out using the built-in Multifunctional detectors. For
 parallel geometries this requires a special scoring physics process.
 See examples/extended/runAndEvent (especailly RE05) for clarification.
 
 Known problems - should not happen
 ----------------------------------
 In the following scenario it can happen that a particle is not
 biased and it's weight is therefore not changed even if it crosses
 a boundary where biasing should happen.
 Importance and weight window sampling create particles on boundaries 
 between volumes. If the GPIL method of a physical process returns 
 0 as step length for a particle on a boundary and if the PostStepDoIt of
 that process changes the direction of the particle to go back in the 
 former volume the biasing won't be invoked. 
 This will produce particles with weights that do not correspondent to the
 importance of the current volumes.
 
 Further information:
 --------------------
 Short description of importance sampling and scoring:
 https://geant4.web.cern.ch/collaboration/working_groups/geometryTransport/#development-documents (Under the Event Biasing & Tallies Section)
 
 Example B01
 ===========
 
 The example uses importance sampling or the weight window technique 
 according to an input parameter. It uses scoring in both cases. 
 Importance values or weight windows are defined according to the mass 
 geometry. In this example the weight window technique is configured such 
 that it behaves equivalent to importance sampling: The window is actually 
 not a window but simply the inverse of the importance value and only
 one energy region is used that covers all energies in the problem.
 The user may change the weight window configuration by changing the
 initialization of the weight window algorithm in example,cc. 
 Different energy bounds for the weight window technique may be specified 
 in B01DetectorConstruction.
 
 The executable takes one optional argument: 0 or 1. Without argument or
 with argument: 0, the importance sampling is applied with argument: 1,
 the weight window technique is applied.
 
 A modular approach is applied to the physicslist and the extension for biasing.
 
 Example B02
 ===========
 
 This example uses a parallel geometry to define G4GeometryCell objects
 for scoring and importance sampling. The output should be equivalent to B01.
 
 A modular approach is applied to the physicslist and the extension for biasing.
 The parallel geometry is included in this extension.
 
 Example B03
 ===========
 
 This example uses a parallel geometry to define G4GeometryCell objects
 for scoring and importance sampling. The output should be statistically 
 equivalent to B02 (and B01).
 
 This demonstrates a customised "flat" physics implementation with the addition
 of biasing. Complementary approach to the modular physics lists of B01 and B02
 
 
  ___________________________________________________________________________
 
 
                   Generic biasing examples GB01 - GB06
                   ------------------------------------
 
 These examples illustrate the usage of a biasing scheme implemented since
 version Geant4 10.0.
 The scheme is meant to be extensible, not limited to these six examples.
 
 Example GB01:
 =============
 
 This example illustrates how to bias process cross-sections in this scheme.
 
 
 Example GB02:
 =============
 
 Illustrates a force collision scheme similar to the MCNP one.
 
 
 Example GB03:
 =============
 
 Illustrates geometry based biasing.
 
 
 Example GB04:
 =============
 
 Illustrates a bremsstrahlung splitting.
 
 
 Example GB05:
 =============
 
 Illustrates a "splitting by cross-section" technique: a splitting-based
 technique using absorption cross-section to control the neutron population.
 
 
 Example GB06:
 =============
 
 Illustrates the usage of parallel geometries with generic biasing.
 
 Example GB07:
 =============
 
 Illustrates the usage of leading particle biasing with generic biasing.
 
 
  ___________________________________________________________________________
 
 
              Reverse MonteCarlo Technique example: ReverseMC01
              -------------------------------------------------
 
 Example ReverseMC01
 ===================
 
 Example illustrating the use of the Reverse Monte Carlo (RMC) mode in a Geant4
 application. See details in ReverseMC01/README.
 

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