The MinMax information measure
The importance of finding minimum entropy probability distributions and the value of minimum entropy for a probabilistic system is discussed. A method to calculate these when there are both moment and inequality constraints on probabilities is given and illustrated with examples. It is shown that: i...
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| Veröffentlicht in: | International journal of systems science 1995-01, Vol.26 (1), p.1-12 |
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| container_end_page | 12 |
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| container_issue | 1 |
| container_start_page | 1 |
| container_title | International journal of systems science |
| container_volume | 26 |
| creator | KAPUR, J. N. BACIU, G. KESAVAN, H. K. |
| description | The importance of finding minimum entropy probability distributions and the value of minimum entropy for a probabilistic system is discussed. A method to calculate these when there are both moment and inequality constraints on probabilities is given and illustrated with examples. It is shown that: information given by moments or inequalities on probabilities can be measured by the reduction in the uncertainty gap (S
max
- S
min
); and in certain circumstances the inequalities on probabilities can provide significant information about probabilistic systems. |
| doi_str_mv | 10.1080/00207729508929020 |
| format | Article |
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max
- S
min
); and in certain circumstances the inequalities on probabilities can provide significant information about probabilistic systems.</description><identifier>ISSN: 0020-7721</identifier><identifier>EISSN: 1464-5319</identifier><identifier>DOI: 10.1080/00207729508929020</identifier><identifier>CODEN: IJSYA9</identifier><language>eng</language><publisher>London: Taylor & Francis Group</publisher><subject>Applied sciences ; Exact sciences and technology ; Information theory ; Information, signal and communications theory ; Telecommunications and information theory</subject><ispartof>International journal of systems science, 1995-01, Vol.26 (1), p.1-12</ispartof><rights>Copyright Taylor & Francis Group, LLC 1995</rights><rights>1995 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c325t-293d6b10abb11df8fbc23029edba5675a27533e7dd0f470837f6475fbe92a19e3</citedby><cites>FETCH-LOGICAL-c325t-293d6b10abb11df8fbc23029edba5675a27533e7dd0f470837f6475fbe92a19e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.tandfonline.com/doi/pdf/10.1080/00207729508929020$$EPDF$$P50$$Ginformaworld$$H</linktopdf><linktohtml>$$Uhttps://www.tandfonline.com/doi/full/10.1080/00207729508929020$$EHTML$$P50$$Ginformaworld$$H</linktohtml><link.rule.ids>314,780,784,4024,27923,27924,27925,59647,60436</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=3470291$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>KAPUR, J. N.</creatorcontrib><creatorcontrib>BACIU, G.</creatorcontrib><creatorcontrib>KESAVAN, H. K.</creatorcontrib><title>The MinMax information measure</title><title>International journal of systems science</title><description>The importance of finding minimum entropy probability distributions and the value of minimum entropy for a probabilistic system is discussed. A method to calculate these when there are both moment and inequality constraints on probabilities is given and illustrated with examples. It is shown that: information given by moments or inequalities on probabilities can be measured by the reduction in the uncertainty gap (S
max
- S
min
); and in certain circumstances the inequalities on probabilities can provide significant information about probabilistic systems.</description><subject>Applied sciences</subject><subject>Exact sciences and technology</subject><subject>Information theory</subject><subject>Information, signal and communications theory</subject><subject>Telecommunications and information theory</subject><issn>0020-7721</issn><issn>1464-5319</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1995</creationdate><recordtype>article</recordtype><recordid>eNp1j01LxDAQhoMoWFd_gBfZg9fqTNI0DXiRxVVhFy_ruUyaBCv9WJKK7r-3S1cv4mkY5nnm5WXsEuEGoYBbAA5KcS2h0FyPyxFLMMuzVArUxyzZ39MRwFN2FuM7AEjJIWFXmzc3X9fdmr7mdef70NJQ9928dRQ_gjtnJ56a6C4Oc8Zelw-bxVO6enl8Xtyv0kpwOaRcC5sbBDIG0frCm4oL4NpZQzJXkriSQjhlLfhMQSGUzzMlvXGaE2onZgynv1XoYwzOl9tQtxR2JUK5L1j-KTg615OzpVhR4wN1VR1_RTEGcY0jdjdhh3qffWhsOdCu6cOPI_5P-QYb7mDJ</recordid><startdate>19950101</startdate><enddate>19950101</enddate><creator>KAPUR, J. N.</creator><creator>BACIU, G.</creator><creator>KESAVAN, H. K.</creator><general>Taylor & Francis Group</general><general>Taylor & Francis</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>19950101</creationdate><title>The MinMax information measure</title><author>KAPUR, J. N. ; BACIU, G. ; KESAVAN, H. K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c325t-293d6b10abb11df8fbc23029edba5675a27533e7dd0f470837f6475fbe92a19e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1995</creationdate><topic>Applied sciences</topic><topic>Exact sciences and technology</topic><topic>Information theory</topic><topic>Information, signal and communications theory</topic><topic>Telecommunications and information theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>KAPUR, J. N.</creatorcontrib><creatorcontrib>BACIU, G.</creatorcontrib><creatorcontrib>KESAVAN, H. K.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><jtitle>International journal of systems science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>KAPUR, J. N.</au><au>BACIU, G.</au><au>KESAVAN, H. K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The MinMax information measure</atitle><jtitle>International journal of systems science</jtitle><date>1995-01-01</date><risdate>1995</risdate><volume>26</volume><issue>1</issue><spage>1</spage><epage>12</epage><pages>1-12</pages><issn>0020-7721</issn><eissn>1464-5319</eissn><coden>IJSYA9</coden><abstract>The importance of finding minimum entropy probability distributions and the value of minimum entropy for a probabilistic system is discussed. A method to calculate these when there are both moment and inequality constraints on probabilities is given and illustrated with examples. It is shown that: information given by moments or inequalities on probabilities can be measured by the reduction in the uncertainty gap (S
max
- S
min
); and in certain circumstances the inequalities on probabilities can provide significant information about probabilistic systems.</abstract><cop>London</cop><pub>Taylor & Francis Group</pub><doi>10.1080/00207729508929020</doi><tpages>12</tpages></addata></record> |
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| ispartof | International journal of systems science, 1995-01, Vol.26 (1), p.1-12 |
| issn | 0020-7721 1464-5319 |
| language | eng |
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| source | Access via Taylor & Francis |
| subjects | Applied sciences Exact sciences and technology Information theory Information, signal and communications theory Telecommunications and information theory |
| title | The MinMax information measure |
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