castanyes blaves

1: Syst Biol. 2008 Oct;57(5):665-74.Click here to read Links
Penalized likelihood phylogenetic inference: bridging the parsimony-likelihood gap.
Kim J, Sanderson MJ.

Department of Biology and Penn Genome Frontiers Institute, University of Pennsylvania, Philadelphia, Pennsylvania, USA. junhyong@sas.upenn.edu

The increasing diversity and heterogeneity of molecular data for phylogeny estimation has led to development of complex models and model-based estimators. Here, we propose a penalized likelihood (PL) framework in which the levels of complexity in the underlying model can be smoothly controlled. We demonstrate the PL framework for a four-taxon tree case and investigate its properties. The PL framework yields an estimator in which the majority of currently employed estimators such as the maximum-parsimony estimator, homogeneous likelihood estimator, gamma mixture likelihood estimator, etc., become special cases of a single family of PL estimators. Furthermore, using the appropriate penalty function, the complexity of the underlying models can be partitioned into separately controlled classes allowing flexible control of model complexity.