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Parallel Single Chain in Markov Chain Monte Carlo Simulation
- The Markov Chain Monte Carlo (MCMC) method is a statistical almost experimental approach to computing integral using random positions, called samples, whose distribution is carefully chosen. In this research, a normal distribution model with unknown mean and known variance is considered. Posterior statistics are computed using the sample mean and standard deviation, as well as the prior mean and standard deviation, instead of data input. Because this is a singleparameter model, posterior samples of mean are simulated in parallel by Monte Carlo simulation. This research also presents parallel communication schemes for simulated a single chain in Markov chain using Message Passing Interface (MPI). In this simulation, the number of simulation steps broadcast to all participating processes. Each process computes a partial sum of simulated values. All partial sums are combined into the grand sum. Finally, the root process computes posterior mean and standard variance. A major purpose of this research is to advocate the use of parallelization within a single chain, hence infusing high-performance computing technologies.
Wint Pa Pa Kyaw
- University of Yangon Research Journal