A Discrete-time Particle Filter and Central Limit Theorem
| dc.contributor.advisor | Kouritzin, Mike (Mathematical and Statistical Sciences) | |
| dc.contributor.author | Ye, Zi | |
| dc.contributor.other | Choulli, Tahir (Mathematical and Statistical Sciences) | |
| dc.contributor.other | Berger, Arno (Mathematical and Statistical Sciences) | |
| dc.contributor.other | Wong, Yau Shu (Mathematical and Statistical Sciences) | |
| dc.date.accessioned | 2025-05-28T18:47:57Z | |
| dc.date.available | 2025-05-28T18:47:57Z | |
| dc.date.issued | 2014-06 | |
| dc.description.abstract | We introduce two kinds of particle filters, one is weighted particle filter and the other is resampling particle filter. We prove the Strong Law of Large Numbers and Central Limit Theorem for both particle filters. Then, we show that the resampling particle filter is better than the weighted one. | |
| dc.identifier.doi | https://doi.org/10.7939/R3NT1D | |
| dc.language.iso | en | |
| dc.rights | This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law. | |
| dc.subject | Central Limit Theorem | |
| dc.subject | Particle filters | |
| dc.title | A Discrete-time Particle Filter and Central Limit Theorem | |
| dc.type | http://purl.org/coar/resource_type/c_46ec | |
| thesis.degree.discipline | Applied Mathematics | |
| thesis.degree.grantor | http://id.loc.gov/authorities/names/n79058482 | |
| thesis.degree.level | Master's | |
| thesis.degree.name | Master of Science | |
| ual.date.graduation | Spring 2014 | |
| ual.department | Department of Mathematical and Statistical Sciences | |
| ual.jupiterAccess | http://terms.library.ualberta.ca/public |
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