Piecewise Constant Modeling of Sequential Data Using Reversible Jump Markov Chain Monte Carlo

Summary

We describe the use of reversible jump Markov chain Monte Carlo (RJMCMC) methods for finding piecewise constant descriptions of sequential data. The method provides posterior distributions on the number of segments in the data and thus gives a much broader view on the potential data than do methods (such as dynamic programming) that aim only at finding a single optimal solution. On the other hand, MCMC methods can be more difficult to implement than discrete optimization techniques, and monitoring convergence of the simulations is not trivial. We illustrate the methods by modeling the GC content and distribution of occurrences of ORFs and SNPs along the human genomes. We show how the simple models can be extended by modeling the influence of GC content on the intensity of ORF occurrence.

Keywords

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