Title: Title: Tajima coalescent
In this talk I will present the Tajima coalescent, a model on the ancestral relationships of molecular samples. This model is then used as a prior model on unlabeled genealogies to infer evolutionary parameters with a Bayesian nonparametric method. I will then show that conditionally on observed data and a particular mutation model, the cardinality of the hidden state space of Tajima’s genealogies is exponentially smaller than the cardinality of the hidden state space of Kingman’s genealogies. We estimate the corresponding cardinalities with sequential importance sampling. Finally, I will propose a new distance on unlabeled genealogies that allows us to compare different distributions on unlabeled genealogies to Tajima’s coalescent.
Julia Palacios is assistant professor of Statistics and Biomedical Data Science at Stanford. Before that, She was a postdoctoral researcher jointly at Harvard University and Brown University. She completed her PhD in Statistics at the University of Washington in 2013. Julia seeks to provide statistical answers to data-driven question in population genetics and evolutionary biology. Julia is recipient of the Sloan Research Fellowship and the Terman Fellowship.