<?xml version="1.0" encoding="utf-8"?>
<?xml-stylesheet type="text/xsl" href="style/detail_T.xsl"?>
<bibitem type="J">   <ARLID>0411083</ARLID> <utime>20240103182300.6</utime><mtime>20060210235959.9</mtime>        <title language="eng" primary="1">General framework for multidimensional models</title>  <specification> <page_count>21 s.</page_count> </specification>   <serial><ARLID>cav_un_epca*0256802</ARLID><ISSN>0884-8173</ISSN><title>International Journal of Intelligent Systems</title><part_num/><part_title/><volume_id>18</volume_id><volume>1 (2003)</volume><page_num>107-127</page_num><publisher><place/><name>Wiley</name><year/></publisher></serial>    <keyword>probability</keyword>   <keyword>possibility theory</keyword>   <keyword>multidimensional distribution</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>GA201/02/1269</project_id> <agency>GA ČR</agency> <ARLID>cav_un_auth*0005739</ARLID> </project> <project> <project_id>1999-24</project_id> <agency>AKTION-KONTAKT</agency> <country>AT</country> </project> <research> <research_id>CEZ:AV0Z1075907</research_id> </research>  <abstract language="eng" primary="1">The paper is an attempt to build up a unifying theoretical framework in which both probabilistic and possibilistic multidimensional models could be described. This novel approach is applied to presenting models based on iterative application of operators of composition. In probabilistic framework, the model is fully equivalent to Bayesian networks.</abstract>      <RIV>BA</RIV>   <department>MTR</department>    <permalink>http://hdl.handle.net/11104/0131170</permalink>   <ID_orig>UTIA-B 20030070</ID_orig>       <arlyear>2003</arlyear>       <unknown tag="mrcbU63"> cav_un_epca*0256802 International Journal of Intelligent Systems 0884-8173 1098-111X Roč. 18 č. 1 2003 107 127 Wiley </unknown> </cas_special> </bibitem>