Monte Carlo estimation of total variation distance of Markov chains on large spaces, with application to phylogenetics

Herbei Radu and Kubatko Laura ()
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Herbei Radu: The Ohio State University – Statistics, Columbus, OH, USA
Kubatko Laura: The Ohio State University – Statistics, Columbus, OH, USA

Statistical Applications in Genetics and Molecular Biology, 2013, vol. 12, issue 1, 39-48

Abstract: Markov chains are widely used for modeling in many areas of molecular biology and genetics. As the complexity of such models advances, it becomes increasingly important to assess the rate at which a Markov chain converges to its stationary distribution in order to carry out accurate inference. A common measure of convergence to the stationary distribution is the total variation distance, but this measure can be difficult to compute when the state space of the chain is large. We propose a Monte Carlo method to estimate the total variation distance that can be applied in this situation, and we demonstrate how the method can be efficiently implemented by taking advantage of GPU computing techniques. We apply the method to two Markov chains on the space of phylogenetic trees, and discuss the implications of our findings for the development of algorithms for phylogenetic inference.

Keywords: GPU computing; mixing time; phylogenetics; total variation distance (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1515/sagmb-2012-0023

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