Geometric Allocation Approach in Markov Chain Monte Carlo

Chapter
Part of the Springer Theses book series (Springer Theses)

Abstract

The general improvement of the Markov chain Monte Carlo method, which is a powerful tool for investigating systems with multiple degrees of freedom, will be shown in this chapter. There are three key points for the method to be effective: the choice of the ensemble, the selection of candidate configurations, and the determination of the transition probability (kernel). We focus our interest on the third optimization problem. After reviewing the previous work, we will invent a novel method that constructs a transition kernel by a geometric approach. This method finds solutions through a graphical procedure, namely weight allocation, instead of solving the detailed balance equation algebraically as before. Surprisingly, it is always possible to find a solution that minimizes the average rejection rate. Later, this geometric approach will be extended to continuous state space. We will assess the convergence and the autocorrelations for the ferromagnetic q-state Potts models and some simple models with continuous state variables. Then we will see the significant improvement by the new method, comparing to the conventional methods, such as the Metropolis and the heat bath algorithm. Our method is the first versatile scheme that satisfies the total balance without imposing the detailed balance.

Keywords

Breaking of detailed balance Irreversible Markov chain Optimization of transition kernel Geometric weight allocation 

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Copyright information

© Springer Japan 2014

Authors and Affiliations

  1. 1.Department of PhysicsBoston UniversityBostonUSA

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