Seriation in Paleontological Data Using Markov Chain Monte Carlo Methods

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Puolamäki, Kai Fortelius, Mikael Mannila, Heikki 2017-10-15T20:55:06Z 2017-10-15T20:55:06Z 2006
dc.identifier.citation Puolamäki , K , Fortelius , M & Mannila , H 2006 , ' Seriation in Paleontological Data Using Markov Chain Monte Carlo Methods ' PLoS Computational Biology , vol 2 , no. 2 , e6 , pp. 62-70 . DOI: 10.1371/journal.pcbi.0020006 en
dc.identifier.issn 1553-7358
dc.identifier.other PURE UUID: bd7c5d9c-5bf5-4acc-8195-57ab7c082d30
dc.identifier.other PURE ITEMURL:
dc.identifier.other PURE LINK:
dc.identifier.other PURE FILEURL:
dc.description.abstract Given a collection of fossil sites with data about the taxa that occur in each site, the task in biochronology is to find good estimates for the ages or ordering of sites. We describe a full probabilistic model for fossil data. The parameters of the model are natural: the ordering of the sites, the origination and extinction times for each taxon, and the probabilities of different types of errors. We show that the posterior distributions of these parameters can be estimated reliably by using Markov chain Monte Carlo techniques. The posterior distributions of the model parameters can be used to answer many different questions about the data, including seriation (finding the best ordering of the sites) and outlier detection. We demonstrate the usefulness of the model and estimation method on synthetic data and on real data on large late Cenozoic mammals. As an example, for the sites with large number of occurrences of common genera, our methods give orderings, whose correlation with geochronologic ages is 0.95. en
dc.format.extent 62-70
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries PLoS Computational Biology en
dc.relation.ispartofseries Volume 2, issue 2 en
dc.rights openAccess en
dc.title Seriation in Paleontological Data Using Markov Chain Monte Carlo Methods en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.identifier.urn URN:NBN:fi:aalto-201710157147
dc.identifier.doi 10.1371/journal.pcbi.0020006
dc.type.version publishedVersion

Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search archive

Advanced Search

article-iconSubmit a publication


My Account