UCLA

A birth-death process is a continuous-time Markov chain that counts the number of particles in a system over time. Each particle can give birth to another particle or die, and the rate of births and deaths at any given time depends on how many extant particles there are. Birth-death processes are popular modeling tools in evolution, population biology, genetics, epidemiology, and ecology. Despite the widespread interest in birth-death models, no efficient method exists to evaluate the finite-time transition probabilities in a process with arbitrary birth and death rates. Statistical inference of the instantaneous particle birth and death rates also remains largely limited to continuously-observed processes in which per-particle birth and death rates are constant. The lack of theoretical progress in developing statistical tools for dealing with data from birth-death processes has hindered their adoption by applied researchers, and represents a major research frontier in statistical inference for stochastic processes. In this dissertation, I seek to fill this apparent void in three ways. First, I develop mathematical theory and computational tools for computing transition probabilities for general birth-death processes. Second, I develop algorithms for maximum likelihood estimation of rate parameters in discretely observed processes. Third, I derive probability distributions for characteristics of certain birth-death models that are fundamental in macroevolutionary studies. In each case, I give practical applications of the methodology, and show how unsolved problems can be attacked using these techniques.