Influence modeling of complex stochastic processes

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.

Department dc:contributor.department

Program in Media Arts and Sciences (Massachusetts Institute of Technology)

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Dong, Wen

Advisor dc:contributor.advisor

• Alex (Sandy) Pentland.

• Architecture. Program In Media Arts and Sciences

Statement 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/37386

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/37386