Monte Carlo estimation of total variation distance of Markov chains on large spaces, with application to phylogenetics
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Radu Herbei
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.
We acknowledge computing support from the Ohio Supercomputer Center (http://www.osc.edu/).
Conflict of interest statement
Funding: The first author is supported in part by the National Science Foundation award DMS-1209142. The second author is supported in part by the National Science Foundation award DMS-1106706.
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©2013 by Walter de Gruyter Berlin Boston
Articles in the same Issue
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- Approximate Bayesian computation with functional statistics
- Monte Carlo estimation of total variation distance of Markov chains on large spaces, with application to phylogenetics
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Articles in the same Issue
- Studying the evolution of transcription factor binding events using multi-species ChIP-Seq data
- Approximate Bayesian computation with functional statistics
- Monte Carlo estimation of total variation distance of Markov chains on large spaces, with application to phylogenetics
- Higher order asymptotics for negative binomial regression inferences from RNA-sequencing data
- Flexible pooling in gene expression profiles: design and statistical modeling of experiments for unbiased contrasts
- On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo
- Inferring latent gene regulatory network kinetics
