Representing parametric probabilistic models tainted with imprecision
Numerical possibility theory, belief functions have been suggested as useful tools to represent imprecise, vague or incomplete information. They are particularly appropriate in uncertainty analysis where information is typically tainted with imprecision or incompleteness. Based on their experience o...
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| Veröffentlicht in: | Fuzzy sets and systems 2008-08, Vol.159 (15), p.1913-1928 |
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| container_end_page | 1928 |
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| container_issue | 15 |
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| container_title | Fuzzy sets and systems |
| container_volume | 159 |
| creator | Baudrit, C. Dubois, D. Perrot, N. |
| description | Numerical possibility theory, belief functions have been suggested as useful tools to represent imprecise, vague or incomplete information. They are particularly appropriate in uncertainty analysis where information is typically tainted with imprecision or incompleteness. Based on their experience or their knowledge about a random phenomenon, experts can sometimes provide a class of distributions without being able to precisely specify the parameters of a probability model. Frequentists use two-dimensional Monte-Carlo simulation to account for imprecision associated with the parameters of probability models. They hence hope to discover how variability and imprecision interact. This paper presents the limitations and disadvantages of this approach and propose a fuzzy random variable approach to treat this kind of knowledge. |
| doi_str_mv | 10.1016/j.fss.2008.02.013 |
| format | Article |
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This paper presents the limitations and disadvantages of this approach and propose a fuzzy random variable approach to treat this kind of knowledge.</description><subject>Belief functions</subject><subject>Chemical and Process Engineering</subject><subject>Computer Science</subject><subject>Engineering Sciences</subject><subject>Food engineering</subject><subject>Fuzzy random variable</subject><subject>Imprecise probabilities</subject><subject>Life Sciences</subject><subject>Monte-Carlo 2D</subject><subject>Possibility</subject><subject>Probability-Boxes</subject><issn>0165-0114</issn><issn>1872-6801</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKs_wNtePew6SbbZLZ5KqR9QEETPIcnO2in7RRIq_ntTKh49zQfvMzAPY7ccCg5c3e-LNoRCANQFiAK4PGMzXlciVzXwczZLmUUOnJeX7CqEPUDqFczY5g0njwGHSMNnNhlveoyeXDb50RpLHYWYpn5ssAtZNDREbLIviruM-kQ6CjQO1-yiNV3Am986Zx-Pm_f1c759fXpZr7a5k5LH3Km6bZ1qFlZVS1NZbGVt1BKbUnAH1nLrlEGQwtrKWitsK0pwxjk0lSzRyDm7O93dmU5Pnnrjv_VoSD-vtvq4A6GUlGV94CnLT1nnxxA8tn8AB310pvc6OdNHZ4nTyVliHk5MehYPhF4HRzg4bCi9GnUz0j_0D7gWdsU</recordid><startdate>20080801</startdate><enddate>20080801</enddate><creator>Baudrit, C.</creator><creator>Dubois, D.</creator><creator>Perrot, N.</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>AAYXX</scope><scope>CITATION</scope><scope>1XC</scope><orcidid>https://orcid.org/0000-0003-4320-3345</orcidid><orcidid>https://orcid.org/0000-0003-3795-0838</orcidid><orcidid>https://orcid.org/0000-0002-6505-2536</orcidid></search><sort><creationdate>20080801</creationdate><title>Representing parametric probabilistic models tainted with imprecision</title><author>Baudrit, C. ; Dubois, D. ; Perrot, N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c331t-c68ffc6d5b679a7bef38a69ed421c0bb1bc6ae032bb7bbb2bf240caccea734ea3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Belief functions</topic><topic>Chemical and Process Engineering</topic><topic>Computer Science</topic><topic>Engineering Sciences</topic><topic>Food engineering</topic><topic>Fuzzy random variable</topic><topic>Imprecise probabilities</topic><topic>Life Sciences</topic><topic>Monte-Carlo 2D</topic><topic>Possibility</topic><topic>Probability-Boxes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Baudrit, C.</creatorcontrib><creatorcontrib>Dubois, D.</creatorcontrib><creatorcontrib>Perrot, N.</creatorcontrib><collection>CrossRef</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Fuzzy sets and systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Baudrit, C.</au><au>Dubois, D.</au><au>Perrot, N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Representing parametric probabilistic models tainted with imprecision</atitle><jtitle>Fuzzy sets and systems</jtitle><date>2008-08-01</date><risdate>2008</risdate><volume>159</volume><issue>15</issue><spage>1913</spage><epage>1928</epage><pages>1913-1928</pages><issn>0165-0114</issn><eissn>1872-6801</eissn><abstract>Numerical possibility theory, belief functions have been suggested as useful tools to represent imprecise, vague or incomplete information. 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| language | eng |
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| source | Access via ScienceDirect (Elsevier) |
| subjects | Belief functions Chemical and Process Engineering Computer Science Engineering Sciences Food engineering Fuzzy random variable Imprecise probabilities Life Sciences Monte-Carlo 2D Possibility Probability-Boxes |
| title | Representing parametric probabilistic models tainted with imprecision |
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