Graphical model, the marriage between graph theory and probability theory, has been drawing increasing attention because of its many attractive features. In this paper, we consider the problem of learning the structure of graphical model based on observed data through a greedy forward-backward algorithm and with the use of learned model to classify the data into different categories. We establish the graphical model associated with a binary Ising Markov random field. And model selection is implemented by adding and deleting edges between nodes. Our experiments show that: compared with previous methods, the proposed algorithm has better performance in terms of correctness rate and model selection. / text

I apply the GMTS approach to graphical modelling of time series to data sets from economics, ecology and environmental science. This approach improves on traditional approaches to modelling insofar as it selects the most parsimonius model. I improve on this approach by removing some redundancies in the GMTS approach. However, a bias in terms of which links are selected means that it is unlikely that this model will select the best causal model.

Thesis (M.Sc.)--York University, 2005. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 123-127). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR11829

Thesis (M.S. in Information Systems) Naval Postgraduate School, March 1992. / Thesis Advisor: Bui, Tung X. "March 1992." Includes bibliographical references (p. 66). Also available in print.

We describe the application of conditional random fields (CRF) to physiological data modeling for the application of activity recognition. We use the data provided by the Physiological Data Modeling Contest (PDMC), a Workshop at ICML 2004. Data used in PDMC are sequential in nature: they consist of physiological sessions, and each session consists of minute-by-minute sensor readings. We show that linear chain CRF can effectively make use of the sequential information in the data, and, with Expectation Maximization, can be trained on partially unlabeled sessions to improve performance. We also formulate a mixture CRF to make use of the identities of the human subjects to further improve performance. We propose that mixture CRF can be used for transfer learning, where models can be trained on data from different domains. During testing, if the domain of the test data is known, it can be used to instantiate the mixture node, and when it is unknown (or when it is a completely new domain), the marginal probabilities of the labels over all training domains can still be used effectively for prediction. / Singapore-MIT Alliance (SMA)

archives@tulane.edu / With the rapid development of precision medicine across almost all areas of medicine, the Research Domain Criteria (RDoC) project has been initiated to develop data-driven matrices toward precision medicine for mental disorder by integrating multilevel information including genomics, molecules, circuits, and behaviors. This thesis, under the guidance of the RDoC framework, aims to gain a more complete understanding of the role of oscillatory behavior and network connectivity in normal/abnormal brain functioning and cognitive development. Two specific topics were involved: 1. Understand the complex mechanism for mental disorder through multiomics data; 2. Study the development of FC from childhood to adulthood using multi-paradigm brain images. We intend to identify new and reliable biomarkers for the purpose of precise diagnosis and can potentially provide an enormous impetus for drug discovery through the comparison of normal and abnormal brains and the investigation of dynamic changes. This thesis proposes several new analytic graphical models (directed and undirected) to assess brain functional connectivity (FC), each targeting a specific problem in the biomedical applications: the psi-learning method to resolve the high dimensionality for networks on voxel level, the latent Gaussian copula model for mix data distributions, the joint Bayesian incorporating estimation to address heterogeneities in undirected graphical models; the psi-LiNGAM and BiLiNGAM for the situations of small sample size and heterogeneities in directed acyclic graphs, respectively. The proposed methods are validated through a series of simulation studies and large genomic and neuroimaging datasets, where they confirm results from previous studies and lead to new biological insights. In addition, we put extra efforts on promoting reproducible research and make the proposed methods widely available to the scientific community by the release of free and open-source codes. / 1 / Aiying Zhang

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ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / This paper describes a telemetry system used in the Missouri S&T solar car, which competed in the American Solar Challenge. The system monitors parameters of a number of the on-board electronic and mechanical systems, and also the activities of the vehicle driver. This data is transmitted to a lead vehicle, where the support team analyzes the performance in real-time to optimize the vehicle's performance. In previous vehicles the data was displayed using a LabVIEW based user interface. In this work we will describe a custom software solution, which provides the team with additional flexibility to display and analyze the data.

This master’s report seeks to increase knowledge of complex numbers and how to identify complex roots graphically. The reader will obtain a greater understanding of the history of complex numbers, the definition of a complex number and a few of the field properties of complex numbers. Readers will also be enlightened on how to visibly detect complex roots of polynomials of the second, third and fourth degree. / text

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