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dc.contributor.advisor Hartemink, Alexander J en_US
dc.contributor.author Hongo, Yasunori en_US
dc.date.accessioned 2012-09-04T13:15:57Z
dc.date.available 2012-09-04T13:15:57Z
dc.date.issued 2012 en_US
dc.identifier.uri http://hdl.handle.net/10161/5839
dc.description Thesis en_US
dc.description.abstract <p>In this paper, we propose a new learning algorithm for non-stationary Dynamic Bayesian Networks is proposed. Although a number of effective learning algorithms for non-stationary DBNs have previously been proposed and applied in Signal Pro- cessing and Computational Biology, those algorithms are based on batch learning algorithms that cannot be applied to online time-series data. Therefore, we propose a learning algorithm based on a Particle Filtering approach so that we can apply that algorithm to online time-series data. To evaluate our algorithm, we apply it to the simulated data set and the real-world financial data set. The result on the simulated data set shows that our algorithm performs accurately makes estimation and detects change. The result applying our algorithm to the real-world financial data set shows several features, which are suggested in previous research that also implies the effectiveness of our algorithm.</p> en_US
dc.subject Artificial intelligence en_US
dc.subject Finance en_US
dc.subject Computer science en_US
dc.subject Bayesian networks en_US
dc.subject GARCH en_US
dc.subject Monte Carlo methods en_US
dc.subject Particle filtering en_US
dc.subject structure learning en_US
dc.subject volatilities en_US
dc.title Online Learning of Non-Stationary Networks, with Application to Financial Data en_US
dc.type Thesis en_US
dc.department Computer Science en_US

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