Computational Methods for Exploring Human Behavior
Researchers and analysts from many diverse fields are interested in unstructured observations
of human behavior; this variety of data is constantly increasing in quantity. In this dissertation,
we describe a suite of computational methods to assist investigators in interpreting, organizing,
and exploring this data.
We develop two Bayesian latent variable models for human-centered applications; specifically,
we rely on additive Poisson models, which allow behavior to be associated with
various sources of influence. Given observed data, we estimate the posterior distributions of
these models with scalable variational inference algorithms. These models and inference
algorithms are validated on real-world data.
Developing statistical models and corresponding inference algorithms only addresses part of
the needs of investigators. Non-technical researchers faced with analyzing large quantities
of human behavior data are not able to use the results of inference algorithms without tools
to translate estimated posterior distributions into accessible visualizations, browsers, or
navigators. We present visualization based on an underlying statistical model as a first-class
research problem, and provide principles to guide the construction of these systems. We
demonstrate these principles with exploratory tools for two latent variable models.
By considering the interplay between developing statistical models and tools for visualization,
we are able to develop computational methods that provide for the full needs of investigators
interested in exploring human behavior.