Hessian Calculation for Phylogenetic Likelihood based on the Pruning Algorithm and its Applications
Kenney Toby and
Gu Hong
Additional contact information
Kenney Toby: Dalhousie University
Gu Hong: Dalhousie University
Statistical Applications in Genetics and Molecular Biology, 2012, vol. 11, issue 4, 46
Abstract:
We analytically derive the first and second derivatives of the likelihood in maximum likelihood methods for phylogeny. These results enable the Newton-Raphson method to be used for maximising likelihood, which is important because there is a need for faster methods for optimisation of parameters in maximum likelihood methods. Furthermore, the calculation of the Hessian matrix also opens up possibilities for standard likelihood theory to be applied, for inference in phylogeny and for model selection problems. Another application of the Hessian matrix is local influence analysis, which can be used for detecting a number of biologically interesting phenomena. The pruning algorithm has been used to speed up computation of likelihoods for a tree. We explain how it can be used to speed up the computation for the first and second derivatives of the likelihood with respect to branch lengths and other parameters. The results in this paper apply not only to bifurcating trees, but also to general multifurcating trees. We demonstrate the use of our Hessian calculation for the three applications listed above, and compare with existing methods for those applications.
Keywords: phylogeny; likelihood; Newton-Raphson; Markov process; Hessian (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://doi.org/10.1515/1544-6115.1779 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:11:y:2012:i:4:n:14
Ordering information: This journal article can be ordered from
https://www.degruyter.com/view/j/sagmb
DOI: 10.1515/1544-6115.1779
Access Statistics for this article
Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf
More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().