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<bibitem type="D">   <ARLID>0323418</ARLID> <utime>20240111140718.0</utime><mtime>20090422235959.9</mtime>         <title language="eng" primary="1">Estimation of Models with Uniform Innovations and its Application on Traffic Data</title>  <publisher> <place>Praha</place> <name>Czech Technical University in Prague</name> <pub_time>2008</pub_time> </publisher> <specification> <page_count>95 s.</page_count> <media_type>www</media_type> </specification>   <title language="cze" primary="0">Odhadování modelu s rovnoměrně rozloženými inovacemi s aplikací na dopravní data</title>    <keyword>state model</keyword>   <keyword>uniform innovations</keyword>   <keyword>state filtration</keyword>   <keyword>parameter estimation</keyword>    <author primary="1"> <ARLID>cav_un_auth*0101175</ARLID> <name1>Pavelková</name1> <name2>Lenka</name2> <institution>UTIA-B</institution> <full_dept>Department of Adaptive Systems</full_dept>  <fullinstit>Ústav teorie informace a automatizace AV ČR, v. v. i.</fullinstit> </author>   <source> <source_type>pdf</source_type> <url>http://library.utia.cas.cz/separaty/2009/AS/pavelkova-estimation of models with uniform innovations and its application on traffic data-thesis.pdf</url> </source>        <cas_special> <project> <project_id>1M0572</project_id> <agency>GA MŠk</agency> <ARLID>cav_un_auth*0001814</ARLID> </project> <research> <research_id>CEZ:AV0Z10750506</research_id> </research>  <abstract language="eng" primary="1">State estimation is  an important subtask of a range  decision making problems. Kalman filter  is a standard method of its solving. There, a state model with  normally distributed innovations is used.  An unbounded support of normal distribution may cause troubles in  some applications where real quantities are bounded, e.g. in  transportation problems.    Then, techniques dealing with unknown-but-bounded equation errors  can be applied. The resulting min-max type algorithms are useful but the related decision-making tasks are unnecessarily  difficult because of missing statistical tools.  Above mentioned drawbacks can be avoided  by assuming that the involved innovations  have a distribution with restricted support.    We assume that the innovations of the state model are uniformly  distributed. Under this assumption, straightforward use of  Bayesian approach provides either batch filtering, i.e., state  estimation or batch parameter estimation. </abstract> <abstract language="cze" primary="0">Odhadování stavu je jednou z důležitých  úloh v oblasti teorie rozhodování. Tato úloha se standardně  řeší pomocí Kalmanova filtru. V případech, kdy jsou skutečné veličiny omezené, například v dopravních problémech, mohou nastat potíže způsobené neomezeným suportem normálního rozdělení.  V těchto případech lze použít techniky typu ``unknown-but-bounded errors''.  Výsledné algoritmy ale postrádají statistické nástroje, takže návazné rozhodovací úlohy jsou  nadbytečně složité.</abstract>    <reportyear>2009</reportyear>  <RIV>BC</RIV>     <habilitation> <dates>17.4.2009</dates> <degree>Ph.D.</degree> <institution>Ústav aplikované matematiky, Fakulta dopravní, ČVUT</institution> <place>Na Florenci 25, Praha 1</place> <year>2008</year>  </habilitation>  <permalink>http://hdl.handle.net/11104/0171383</permalink>        <arlyear>2008</arlyear>       <unknown tag="mrcbU10"> 2008 </unknown> <unknown tag="mrcbU10"> Praha Czech Technical University in Prague </unknown> <unknown tag="mrcbU56"> pdf </unknown> </cas_special> </bibitem>