Markov Chain Monte Carlo Algorithms

  • Osamu Maruyama
Part of the Mathematics for Industry book series (MFI, volume 5)


Markov chain Monte Carlo (MCMC) methods are a general framework of algorithms for generating samples from a specified probability distribution. They are useful when direct sampling from the distribution is unknown. This article describes theory of MCMC, presents two typical MCMC algorithms (Metropolis-Hastings and Gibbs sampling) and three tempering methods (simulated tempering, parallel tempering, and simulated annealing), and discusses the application of MCMC methods to a prediction problem in systems biology.


Markov chain Monte Carlo MCMC Metropolis-Hastings Gibbs sampling Simulated tempering Parallel tempering Simulated annealing 


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Copyright information

© Springer Japan 2014

Authors and Affiliations

  1. 1.Institute of Mathematics for IndustryKyushu UniversityNishiku, FukuokaJapan

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