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Bridging trees for posterior inference on ancestral recombination graphs

Heine, K; Beskos, A; Jasra, A; Balding, D; De Iorio, M; (2018) Bridging trees for posterior inference on ancestral recombination graphs. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences , 474 (2220) , Article 20180568. 10.1098/rspa.2018.0568. Green open access

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Abstract

We present a new Markov chain Monte Carlo algorithm, implemented in the software Arbores, for inferring the history of a sample of DNA sequences. Our principal innovation is a bridging procedure, previously applied only for simple stochastic processes, in which the local computations within a bridge can proceed independently of the rest of the DNA sequence, facilitating large-scale parallelization.

Type: Article
Title: Bridging trees for posterior inference on ancestral recombination graphs
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1098/rspa.2018.0568
Publisher version: https://royalsocietypublishing.org/doi/full/10.109...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: tree scanning, Markov chain Monte Carlo, coalescent, ancestral recombination graph, Bayesian inference
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10066173
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