Stochastic Geometry, Data Structures and Applications of Ancestral Selection Graphs
The model of Neuhauser and Krone determines a distribution over a class of graphs of randomly variable vertex number, known as ancestral selection graphs. Because vertices have associated scalar ages, realisations of the ancestral selection graph process have randomly variable dimensions.
A Markov chain Monte Carlo method is used to simulate the posterior distribution for population parameters of interest. The state of the Markov chain Monte Carlo is a random graph, with random dimension and equilibrium distribution equal to the posterior distribution.
The aim of the project is to determine if the data is informative of the selection parameter by fitting the model to synthetic data.
Advisor:Dr. Geoff Nicholls; Associate Professor David Scott
School Location:New Zealand
Source Type:Master's Thesis
Keywords:applied mathematics markov chain monte carlo statistics probability evolutionary biology random graphs ancestral selection graph n coalescent
Date of Publication:01/01/2006