<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> / Thesis
| Identifer | oai:union.ndltd.org:DUKE/oai:dukespace.lib.duke.edu:10161/5839 |
| Date | January 2012 |
| Creators | Hongo, Yasunori |
| Contributors | Hartemink, Alexander J |
| Source Sets | Duke University |
| Detected Language | English |
| Type | Thesis |
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