I am currently working on a mathematical library for the simulation of stochastic processes. I am a researcher in Statistics, hence my limited skills in c++ programming :) Let me introduce the framework briefly. The part of the library I am interested with is very similar to the boost random library. I need to implement it because I need to extend some functionality and I am not confident in modifying the boost random library to extend its behaviour (too complicated for me :P). We have (lazy syntax): PseudoRng<TImpl> //A particular pseduo random number generator QuasiRng<TImpl> // A particualr quasi random number generator GaussianDistribution // A particular statistical distribution Sampler<GaussianDistribution> // The interface (for other components of the library) of a sampler of random numbers distributed as a GaussianDistribution Then I consider: template < class TGenerator, class TDistribution, typename TDistribution::variate_t (TDistribution::*algorithm)( const TGenerator& ) const = &TDistribution::sample_default > class VariateSampler : public Sampler<TDistribution> { ... } Here TGenerator can be PseudoRng<TImpl> or QuasiRng<TImpl>, TDistribution can be GaussianDistribution and the pointer to member function algorithm select the algorithm used to sample. While constructing VariateSampler I would like to: 1) Automatically deduce the type of TGenerator and TDistribution 2) Automatically select a different default for algorithm, for particular TDistribution (like GaussianDistribution,PoissonDistribution....) depending on the fact that TGenerator is of type PseudoRng<TImpl> or QuasiRng<Timpl>. So for every TDistribution two different defaults for algorithm. I think that boost parameter should solve my problem, but I am quite confused about how to implement it, expecially the second point. Could you help me with the basic steps I need to consider? Thank you very much in advance. Regards StephQ