Title :
Minimum message length encoding, evolutionary trees and multiple-alignment
Author :
Allison, L. ; Wallace, C.S. ; Yee, C.N.
Author_Institution :
Dept. of Comput. Sci., Monash Univ., Clayton, Vic., Australia
Abstract :
A method of Bayesian inference known as minimum message length encoding is applied to the inference of an evolutionary-tree and to multiple-alignment for k⩾2 strings. It allows the posterior odds-ratio of two competing hypotheses, for example two trees, to be calculated. A tree that is a good hypothesis forms the basis of a short message describing the strings. The mutation process is modelled by a finite-state machine. It is seen that tree inference and multiple-alignment are intimately connected
Keywords :
Bayes methods; biology; encoding; finite automata; inference mechanisms; information theory; probability; trees (mathematics); Bayesian inference; competing hypotheses; evolutionary trees; finite-state machine; inductive inference; minimum description length; minimum message length encoding; multiple-alignment; mutation; phylogenetic tree; posterior odds-ratio; strings; tree inference; Bayesian methods; Codes; Computer science; Costs; Encoding; Genetic mutations; Maximum likelihood estimation; Parameter estimation; Phylogeny; Testing;
Conference_Titel :
System Sciences, 1992. Proceedings of the Twenty-Fifth Hawaii International Conference on
Conference_Location :
Kauai, HI
Print_ISBN :
0-8186-2420-5
DOI :
10.1109/HICSS.1992.183219
