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<bibitem type="C">   <ARLID>0410782</ARLID> <utime>20240103182238.8</utime><mtime>20060210235959.9</mtime>    <ISBN>0-7695-1417-0</ISBN>         <title language="eng" primary="1">Perfect sequences for belief networks representation</title>  <publisher> <place>Los Alamitos</place> <name>IEEE Computer Society Press</name> <pub_time>2001</pub_time> </publisher> <specification> <page_count>10 s.</page_count> </specification>   <serial><title>Proceedings of the 13th IEEE International Conference on Tools in Artificial Intelligence</title><part_num/><part_title/><page_num>87-96</page_num></serial>    <keyword>belief networks</keyword>   <keyword>probability and possibility theories</keyword>   <keyword>operators of composition</keyword>    <author primary="1"> <ARLID>cav_un_auth*0101118</ARLID> <name1>Jiroušek</name1> <name2>Radim</name2> <institution>UTIA-B</institution> <full_dept>Department of Decision Making Theory</full_dept>  <fullinstit>Ústav teorie informace a automatizace AV ČR, v. v. i.</fullinstit> </author> <author primary="0"> <ARLID>cav_un_auth*0101223</ARLID> <name1>Vejnarová</name1> <name2>Jiřina</name2> <institution>UTIA-B</institution> <full_dept>Department of Decision Making Theory</full_dept>  <fullinstit>Ústav teorie informace a automatizace AV ČR, v. v. i.</fullinstit> </author>     <COSATI>12A</COSATI>    <cas_special> <project> <project_id>OK 403</project_id> <agency>GA MŠk</agency> <ARLID>cav_un_auth*0031602</ARLID> </project> <project> <project_id>KONTAKT 1999-24</project_id> <agency>AKTION</agency> <country>AT</country> </project> <research> <research_id>AV0Z1075907</research_id> </research>  <abstract language="eng" primary="1">Most approaches used to represent multidimensional probability distributions are based on graphical Markov modelling. Here we present another technique - we describe a process by which a multidimensional distribution can be composed from a "generating  sequence" - a sequence of lowdimensional distributions. The main advantage of this approach is that the same apparatus based on operators of composition can be applied for description of both probabilistic and possibilistic models.</abstract>  <action target="WRD"> <ARLID>cav_un_auth*0212887</ARLID> <name>IEEE International Conference on Tools in Artificial Intelligence /13./</name> <place>Dallas</place> <country>US</country> <dates>07.11.2001-09.11.2001</dates>  </action>     <RIV>BA</RIV>   <department>MTR</department>    <permalink>http://hdl.handle.net/11104/0130869</permalink>   <ID_orig>UTIA-B 20010251</ID_orig>     <arlyear>2001</arlyear>       <unknown tag="mrcbU10"> 2001 </unknown> <unknown tag="mrcbU10"> Los Alamitos IEEE Computer Society Press </unknown> <unknown tag="mrcbU12"> 0-7695-1417-0 </unknown> <unknown tag="mrcbU63"> Proceedings of the 13th IEEE International Conference on Tools in Artificial Intelligence 87 96 </unknown> </cas_special> </bibitem>