<Record><identifier xmlns="http://purl.org/dc/elements/1.1/">URN:NBN:SI:DOC-A6FZ9UPG</identifier><date>2023</date><creator>Salman, Issam</creator><creator>Vomlel, Jiří</creator><relation>documents/doc/A/URN_NBN_SI_doc-A6FZ9UPG_001.pdf</relation><relation>documents/doc/A/URN_NBN_SI_doc-A6FZ9UPG_001.txt</relation><format format_type="issue">1</format><format format_type="volume">47</format><format format_type="type">article</format><format format_type="extent">str. 83-96</format><identifier identifier_type="ISSN">0350-5596</identifier><identifier identifier_type="DOI">10.31449/inf.v47i1.4497</identifier><identifier identifier_type="COBISSID">196188163</identifier><identifier identifier_type="URN">URN:NBN:SI:doc-A6FZ9UPG</identifier><language>eng</language><publisher publisher_location="Ljubljana">Slovene Society Informatika</publisher><source>Informatica (Ljubljana)</source><rights>BY</rights><subject language_type_id="slv">Bayesove mreže</subject><subject language_type_id="slv">nepopolni podatki</subject><subject language_type_id="slv">strojno učenje</subject><subject language_type_id="slv">umetna inteligenca</subject><title>Learning the structure of Bayesian networks from incomplete data using a mixture model</title></Record>