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Programming language: Haskell
License: GNU General Public License v3.0 or later
Tags: Math     Buffon    
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README

Buffon machines Build Status Hackage License

Buffon machines is a simple, monadic implementation of Buffon machines [1] meant for perfect simulation of discrete random variables using a discrete oracle of random bits. Buffon machines are implemented as monadic computations consuming random bits, provided by a 32-bit buffered oracle. Bit regeneration and computation composition is handled within the monad itself.

The main purpose of Buffon machines is to provide an experimental framework for discrete random variable generation required in the design and implementation of various combinatorial samplers, such as analytic samplers (a.k.a. Boltzmann samplers). In particular, its goal is to provide tools to perfectly simulate discrete distributions using as few random bits as possible.

The current implementation provides several basic generators discussed in [1]. In particular, it offers perfect generators for geometric, Poisson, and logarithmic distributions with given rational or real (i.e. double-precision floating) parameters, as well as a bit-optimal discrete uniform variable and Bernoulli generators described in [2]. More involved Buffon machines can be compiled using the provided combinators.

General, non-uniform discrete variable generation, in the spirit of Knuth and Yao [3], is also available. However, it should be noted that the current implementation does not achieve optimal average bit consumption, except for a limited number of special cases.

References

[1] Ph. Flajolet, M. Pelletier, M. Soria : “On Buffon Machines and Numbers”, SODA'11 - ACM/SIAM Symposium on Discrete Algorithms, San Francisco, USA, pp. 172-183, (Society for Industrial and Applied Mathematics) (2011)

[2] J. Lumbroso : "Optimal Discrete Uniform Generation from Coin Flips, and Applications".

[3] D. Knuth, A. Yao : "The complexity of nonuniform random number generation", in Algorithms and Complexity: New Directions and Recent Results, Academic Press, (1976)


*Note that all licence references and agreements mentioned in the buffon-machines README section above are relevant to that project's source code only.