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Influence modeling of complex stochastic processes

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dc.contributor.advisor Alex (Sandy) Pentland. en_US Dong, Wen, S.M. Massachusetts Institute of Technology en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences en_US 2007-05-16T18:28:40Z 2007-05-16T18:28:40Z 2006 en_US 2006 en_US
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006. en_US
dc.description Includes bibliographical references (leaves 75-76). en_US
dc.description.abstract A complex stochastic process involving human behaviors or human group behaviors is computationally hard to model with a hidden Markov process. This is because the state space of such behaviors is often a Cartesian product of a large number of constituent probability spaces, and is exponentially large. A sample for those stochastic processes is normally composed of a large collection of heterogeneous constituent samples. How to combine those heterogeneous constituent samples in a consistent and stable way is another difficulty for the hidden Markov process modeling. A latent structure influence process models human behaviors and human group behaviors by emulating the work of a team of experts. In such a team, each expert concentrates on one constituent probability space, investigates one type of constituent samples, and/or employ one type of technique. An expert improves his work by considering the results from the other experts, instead of the raw data for them. Compared with the hidden Markov process, the latent structure influence process is more expressive, more stable to outliers, and less likely to overfit. It can be used to study the interaction of over 100 persons and get good results. en_US
dc.description.abstract (cont.) This thesis is organized in the following way. Chapter 0 reviews the notation and the background concepts necessary to develop this thesis. Chapter 1 describes the intuition behind the latent structure influence process and the situations where it outperforms the other dynamic models. In Chapter 2, we give inference algorithms based on two different interpretations of the influence model. Chapter 3 applies the influence algorithms to various toy data sets and real-world data sets. We hope our demonstrations of the influence modeling could serve as templates for the readers to develop other applications. In Chapter 4, we conclude with the rationale and other considerations for influence modeling. en_US
dc.description.statementofresponsibility by Wen Dong. en_US
dc.format.extent 76 leaves en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.subject Architecture. Program In Media Arts and Sciences en_US
dc.title Influence modeling of complex stochastic processes en_US
dc.type Thesis en_US S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences en_US
dc.identifier.oclc 122905859 en_US

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