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Probabilistic Inference with Generating Functions for Population Dynamics of Unmarked Individuals

30 Sep
Monday, 09/30/2019 11:30am to 1:30pm
PhD Thesis Defense
Speaker: Kevin Winner

Modeling the interactions of different population dynamics (e.g. reproduction, migration) within a population is a challenging problem that underlies numerous ecological research questions. Powerful, interpretable models for population dynamics are key to developing intervention tactics, allocating limited conservation resources, and predicting the impact of uncertain environmental forces on a population. Fortunately, probabilistic graphical models provide a robust mechanistic framework for these kinds of problems. However, in the relatively common case where individuals in the population are unmarked (i.e. indistinguishable from one another), models of the population dynamics naturally contain a deceptively challenging statistical feature: discrete latent variables with unbounded/countably infinite support. Unfortunately, existing inference algorithms for discrete distributions are applicable only for finite distributions and while approximate inference algorithms exist for countably infinite discrete distributions, they are generally unreliable and inefficient. In this work, we develop the first known general-purpose polynomial-time exact inference algorithms for this class of models using a novel representation based on probability generating functions. These methods are flexible, easy to use and significantly faster than existing approximate solutions. We also introduce a novel approximation scheme based on this technique that allows it to gracefully scale to populations well beyond the computational limits of any previously known exact or approximate general-purpose inference algorithm for population dynamics. Finally, we conduct an ecological case study on historical data demonstrating the downstream impact of these advances to a large scale population monitoring setting.

Advisor: Dan Sheldon