Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp012r36v101m
 Title: Computational Methods for Exploring Human Behavior Authors: Chaney, Allison June Barlow Advisors: Blei, David M Contributors: Computer Science Department Keywords: Exploratory Data AnalysisLatent Variable ModelsMachine Learning Subjects: Computer science Issue Date: 2016 Publisher: Princeton, NJ : Princeton University Abstract: 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. URI: http://arks.princeton.edu/ark:/88435/dsp012r36v101m Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: catalog.princeton.edu Type of Material: Academic dissertations (Ph.D.) Language: en Appears in Collections: Computer Science

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