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SINBAD AUTOMATION OF SCIENTIFIC PROCESS: FROM HIDDEN FACTOR ANALYSIS TO THEORY SYNTHESIS
| Title: | SINBAD AUTOMATION OF SCIENTIFIC PROCESS: FROM HIDDEN FACTOR ANALYSIS TO THEORY SYNTHESIS. |
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| Name(s): |
KURSUN, OLCAY, Author Favorov, Oleg V., Committee Chair University of Central Florida, Degree Grantor |
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| Type of Resource: | text | |
| Date Issued: | 2004 | |
| Publisher: | University of Central Florida | |
| Language(s): | English | |
| Abstract/Description: | Modern science is turning to progressively more complex and data-rich subjects, which challenges the existing methods of data analysis and interpretation. Consequently, there is a pressing need for development of ever more powerful methods of extracting order from complex data and for automation of all steps of the scientific process. Virtual Scientist is a set of computational procedures that automate the method of inductive inference to derive a theory from observational data dominated by nonlinear regularities. The procedures utilize SINBAD – a novel computational method of nonlinear factor analysis that is based on the principle of maximization of mutual information among non-overlapping sources (Imax), yielding higher-order features of the data that reveal hidden causal factors controlling the observed phenomena. One major advantage of this approach is that it is not dependent on a particular choice of learning algorithm to use for the computations. The procedures build a theory of the studied subject by finding inferentially useful hidden factors, learning interdependencies among its variables, reconstructing its functional organization, and describing it by a concise graph of inferential relations among its variables. The graph is a quantitative model of the studied subject, capable of performing elaborate deductive inferences and explaining behaviors of the observed variables by behaviors of other such variables and discovered hidden factors. The set of Virtual Scientist procedures is a powerful analytical and theory-building tool designed to be used in research of complex scientific problems characterized by multivariate and nonlinear relations. | |
| Identifier: | CFE0000043 (IID), ucf:46124 (fedora) | |
| Note(s): |
2004-05-01 Ph.D. College of Engineering and Computer Science, School of Computer Science This record was generated from author submitted information. |
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| Subject(s): |
Bayesian Networks Blind Source Separation Causal Relations Concept Acquisition Curse of Dimensionality IMAX Knowledge Representation Nonlinear Factor Analysis Virtual Scientist |
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| Persistent Link to This Record: | http://purl.flvc.org/ucf/fd/CFE0000043 | |
| Restrictions on Access: | public | |
| Host Institution: | UCF |
