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Abstract:
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Advances in sequencing technology continue to deliver increasingly large molecular sequence data sets that are often heavily partitioned in order to accurately model the underlying evolutionary processes. In phylogenetic analyses, partitioning strategies involve estimating conditionally independent models of molecular evolution for different genes and different positions within those genes, requiring a large number of evolutionary parameters to be estimated, leading to an increased computational burden for such analyses. The past two decades have also seen the rise of multi-core processors, both in the CPU and GPU processor markets, enabling massively parallel computations that are not yet fully exploited by many software packages for multipartite analyses. We propose a Markov chain Monte Carlo approach using an adaptive multivariate transition kernel to estimate in parallel a large number of parameters, split across partitioned data, by exploiting multi-core processing. We demonstrate that our approach enables the estimation of these multipartite parameters more efficiently than standard approaches that typically employ a mixture of univariate transition kernels.
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