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Restricted Kalman Filtering: Theory, Methods, and Application
In statistics, the Kalman filter is a mathematical method whose purpose is to use a series of measurements observed over time, containing random variations and other inaccuracies, and produce estimates that tend to be closer to the true unknown values than those that would be based on a single measu...
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Lenguaje: | eng |
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Springer
2012
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Acceso en línea: | https://dx.doi.org/10.1007/978-1-4614-4738-2 http://cds.cern.ch/record/1488410 |
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author | Pizzinga, Adrian |
author_facet | Pizzinga, Adrian |
author_sort | Pizzinga, Adrian |
collection | CERN |
description | In statistics, the Kalman filter is a mathematical method whose purpose is to use a series of measurements observed over time, containing random variations and other inaccuracies, and produce estimates that tend to be closer to the true unknown values than those that would be based on a single measurement alone. This Brief offers developments on Kalman filtering subject to general linear constraints. There are essentially three types of contributions: new proofs for results already established; new results within the subject; and applications in investment analysis and macroeconomics, where th |
id | cern-1488410 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2012 |
publisher | Springer |
record_format | invenio |
spelling | cern-14884102021-04-22T00:11:19Zdoi:10.1007/978-1-4614-4738-2http://cds.cern.ch/record/1488410engPizzinga, AdrianRestricted Kalman Filtering: Theory, Methods, and ApplicationMathematical Physics and MathematicsIn statistics, the Kalman filter is a mathematical method whose purpose is to use a series of measurements observed over time, containing random variations and other inaccuracies, and produce estimates that tend to be closer to the true unknown values than those that would be based on a single measurement alone. This Brief offers developments on Kalman filtering subject to general linear constraints. There are essentially three types of contributions: new proofs for results already established; new results within the subject; and applications in investment analysis and macroeconomics, where thSpringeroai:cds.cern.ch:14884102012 |
spellingShingle | Mathematical Physics and Mathematics Pizzinga, Adrian Restricted Kalman Filtering: Theory, Methods, and Application |
title | Restricted Kalman Filtering: Theory, Methods, and Application |
title_full | Restricted Kalman Filtering: Theory, Methods, and Application |
title_fullStr | Restricted Kalman Filtering: Theory, Methods, and Application |
title_full_unstemmed | Restricted Kalman Filtering: Theory, Methods, and Application |
title_short | Restricted Kalman Filtering: Theory, Methods, and Application |
title_sort | restricted kalman filtering: theory, methods, and application |
topic | Mathematical Physics and Mathematics |
url | https://dx.doi.org/10.1007/978-1-4614-4738-2 http://cds.cern.ch/record/1488410 |
work_keys_str_mv | AT pizzingaadrian restrictedkalmanfilteringtheorymethodsandapplication |