BCL: Balance Condition Library
The goal of the BCL is providing
- C++ reference implementation of the rejection-optimized kernel (ST2010) as well as Metropolis-Hasting and Gibbs sampler in Markov Chain Monte Carlo (MCMC),
- interface for C, Fortran, Python, etc, and
- example and benchmark programs of Markov Chain Monte Carlo simulation based on several MCMC kernels on standard models (Ising, Potts, etc) in the field of statistical physics.
- Release 0.1 (2013/09/12)
- Initial version
- H. Suwa and S. Todo, "Markov Chain Monte Carlo Method without Detailed Balance," Physical Review Letters 105, 120603 (2010).
- Hidemaro Suwa (University of Tokyo)
- Synge Todo (University of Tokyo)