Inference in a partially observed queueing model with applications in ecology

Published in ICML, 2015

We consider the problem of inference in a probabilistic model for transient populations where we wish to learn about arrivals, departures, and population size over all time, but the only available data are periodic counts of the population size at specific observation times. The underlying model arises in queueing theory (as an M_t/G/infty queue) and also in ecological models for short-lived animals such as insects. Our work applies to both systems. Previous work in the ecology literature focused on maximum likelihood estimation and made a simplifying independence assumption that prevents inference over unobserved random variables such as arrivals and departures. The contribution of this paper is to formulate a latent variable model and develop a novel Gibbs sampler based on Markov bases to perform inference using the correct, but intractable, likelihood function. We empirically validate the convergence behavior of our sampler and demonstrate the ability of our model to make much finer-grained inferences than the previous approach.

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Recommended citation: Kevin Winner, Garrett Bernstein, and Daniel Sheldon. Inference in a partially observed queueing model with applications in ecology. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pages 2512-2520. JMLR Workshop and Conference Proceedings, 2015 http://kwinner.github.io/files/WinnerBernsteinSheldon2015.pdf