Much of the early work on pedigree reconstruction relied on a graphical model of inheritance in pedigrees, where the reconstruction algorithms choose pedigree graphs that maximized the likelihood of the observed data. That formulation of the pedigree reconstruction problem is a typical example of parametric structured machine learning where the graphical model of interest is the pedigree model. The work presented here is a departure from parametric methods and develops combinatorial methods for estimating pedigree structures.
(Manuscript revised Apr 20, 2010. Manuscript drafted on May 16, 2008 as part of a class project for CS294-26/STAT260: Computational and Mathematical Population Genetics with Prof. Yun Song.)