Biology – Quantitative Biology – Populations and Evolution
Scientific paper
2011-12-23
Biology
Quantitative Biology
Populations and Evolution
Scientific paper
Stochastic models of evolution (Markov random fields on trivalent trees) generally assume that different characters (different runs of the stochastic process) are independent and identically distributed. In this paper we take the first steps towards addressing dependent characters. Specifically we show that, under certain technical assumptions regarding the evolution of individual characters, we can detect any significant, history independent, correlation between any pair of multistate characters. For the special case of the Cavender-Farris-Neyman (CFN) model on two states with symmetric transition matrices, our analysis needs milder assumptions. To perform the analysis, we need to prove a new concentration result for multistate random variables of a Markov random field on arbitrary trivalent trees: we show that the random variable counting the number of leaves in any particular subset of states has variance that is subquadratic in the number of leaves.
Chakrabarty Deeparnab
Kannan Sampath
No associations
LandOfFree
Variance on the Leaves of a Tree Markov Random Field: Detecting Character Dependencies in Phylogenies does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Variance on the Leaves of a Tree Markov Random Field: Detecting Character Dependencies in Phylogenies, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Variance on the Leaves of a Tree Markov Random Field: Detecting Character Dependencies in Phylogenies will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-191877