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Poster
Probabilistic Inference with Generating Functions for Poisson Latent Variable Models
Kevin Winner · Daniel Sheldon

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #2
in Tuesday Posters »

Graphical models with latent count variables arise in a number of fields. Standard exact inference techniques such as variable elimination and belief propagation do not apply to these models because the latent variables have countably infinite support. As a result, approximations such as truncation or MCMC are employed. We present the first exact inference algorithms for a class of models with latent count variables by developing a novel representation of countably infinite factors as probability generating functions, and then performing variable elimination with generating functions. Our approach is exact, runs in pseudo-polynomial time, and is much faster than existing approximate techniques. It leads to better parameter estimates for problems in population ecology by avoiding error introduced by approximate likelihood computations.

Keywords: (Other) Probabilistic Models and Methods · (Other) Applications · Graphical Models · Time Series Analysis
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Author Information

Kevin Winner (UMass CICS)
Daniel Sheldon (University of Massachusetts Amherst)

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