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Transferable belief model

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1901: 25: 1791:. The open-world assumption is that the coin can be stolen in mid-air, disappear, break apart or otherwise fall sideways so that neither Head nor Tail occurs, so that the power set of {Head,Tail} is considered and there is a decomposition of the overall probability (i.e. 1) of the following form: 2186: 1878: 956: 1351: 1195: 1120: 1789: 1236: 1662: 1018: 1709: 1596: 439: 579: 1064: 373: 657: 2196:
Haenni, R. (2006). "Uncover Dempster's Rule Where It Is Hidden" in: Proceedings of the 9th International Conference on Information Fusion (FUSION 2006), Florence, Italy, 2006.
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Haenni, R. (2006). "Uncover Dempster's Rule Where It Is Hidden" in: Proceedings of the 9th International Conference on Information Fusion (FUSION 2006), Florence, Italy, 2006.
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to stress the fact that those probability functions are based on incomplete data, whose only purpose is a forced decision, e.g. to place a bet. This is in contrast to the
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that relaxes the assumption that all possible outcomes are known. Under the open world assumption Dempster's rule of combination is adapted such that there is no
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Smets Ph. (1993) "An axiomatic justification for the use of belief function to quantify beliefs", IJCAI'93 (Inter. Joint Conf. on AI), Chambery, 598–603
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Bayesian updating the first opinion with the second (or the other way round) implies certainty that the cause is
1926: 1497: 1030: 181:. Doctor 1 says that the patient's illness is very likely to be caused by A (very likely, meaning probability 325: 2136: 1987: 1251: 621: 103: 688: 681:
is the set of unknown outcomes. Consequently, the combination rule underlying the TBM corresponds to
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Zadeh, A., L., (1984) "Review of shafer's a mathematical theory of evidence". AI Magazine, 5(3).
1873:{\displaystyle \Pr(\emptyset )+\Pr({\text{Head}})+\Pr({\text{Tail}})+\Pr({\text{Head,Tail}})=1.} 1383: 588: 523: 237: 2100: 777: 2249: 2224: 2170: 2146: 2073: 2030: 1997: 1359: 265: 111: 750: 723: 450: 1459: 280: 2212: 1389: 662: 2099:
Shafer, Glenn (1976). "A Mathematical Theory of Evidence", Princeton University Press,
951:{\displaystyle m_{1,2}(A)=(m_{1}\otimes m_{2})(A)=\sum _{B\cap C=A}m_{1}(B)m_{2}(C)\,\!} 2300: 1439: 1419: 500: 480: 302: 2294: 2174: 2077: 90:
is true from other propositions that are assigned probabilities. It was developed by
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holds true. The underlying idea is that the frame of discernment is not necessarily
130:. This adaptation violates the probabilistic character of the original DST and also 2182: 2127:
Ph, Smets (1990). "The combination of evidence in the transferable belief model".
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Ph, Smets (1990). "The combination of evidence in the transferable belief model".
1346:{\displaystyle P_{\text{Bet}}(x)=\sum _{x\in A\subseteq X}{\frac {m(A)}{|A|}}\,\!} 2276: 169:
problem. A patient has an illness that can be caused by three different factors
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are equally distributed among the atoms of A. This strategy corresponds to the
1190:{\displaystyle \operatorname {bel} (H)=\sum _{\emptyset \neq A\subseteq H}m(A)} 1737:
When tossing a coin one usually assumes that Head or Tail will occur, so that
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is not likely. (For this reason the standard Bayesian approach is to adopt
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Uncertainty Management in Information Systems: from Needs to solutions
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provides insufficient context for those unfamiliar with the subject
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Smets, Ph.; Kennes, R. (1994). "The transferable belief model".
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Smets, Ph.; Kennes, R. (1994). "The transferable belief model".
2213:"Application of transferable belief model to navigation system" 2129:
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
1894: 18: 224:, since although the two doctors point at different causes, 1725:
described above, whose purpose is representing the actual
1115:{\displaystyle \operatorname {bel} :2^{X}\rightarrow \,\!} 82:(DST), which is a mathematical model used to evaluate the 209:= 0.05). How is one to make one's own opinion from this? 102:. In contrast to the original DST the TBM propagates the 2187:
The transferable belief model for belief representation
1784:{\displaystyle \Pr({\text{Head}})+\Pr({\text{Tail}})=1} 1231:{\displaystyle \operatorname {bel} (\emptyset )=0.\,\!} 39: 16:
Model for reasoning with uncertain beliefs and evidence
134:. Therefore, the authors substituted notation such as 1800: 1743: 1673: 1657:{\displaystyle \sum _{x\in X}P_{\text{Bet}}(x)=1\,\!} 1610: 1551: 1517: 1462: 1442: 1422: 1392: 1362: 1263: 1204: 1134: 1075: 1033: 970: 816: 780: 753: 726: 691: 665: 624: 591: 546: 526: 503: 483: 453: 387: 328: 305: 193:= 0.05). Doctor 2 says that the cause is very likely 118:
is taken to indicate an unexpected outcome, e.g. the
1872: 1783: 1703: 1656: 1590: 1530: 1477: 1448: 1428: 1408: 1374: 1345: 1230: 1189: 1114: 1058: 1013:{\displaystyle A,B,C\in 2^{X}\neq \emptyset .\,\!} 1012: 950: 799: 766: 739: 720:. Hence, in the TBM any two independent functions 712: 673: 651: 610: 573: 532: 509: 489: 466: 433: 367: 311: 1700: 1653: 1587: 1342: 1227: 1111: 1009: 947: 430: 364: 2238:"The Dempster–Shafer calculus for statisticians" 2017:"The Dempster–Shafer calculus for statisticians" 1850: 1833: 1816: 1801: 1761: 1744: 1704:{\displaystyle P_{\text{Bet}}(\emptyset )=0\,\!} 1591:{\displaystyle P_{\text{Bet}}:X\rightarrow \,\!} 540:is not required to be zero, and hence generally 240:and avoid the use of 0 or 1 as probabilities.) 2242:International Journal of Approximate Reasoning 2022:International Journal of Approximate Reasoning 2211:Touil, K.; Zribi, M.; Benjelloun, M. (2007). 2199:Ramasso, E., Rombaut, M., Pellerin D. (2007) 585:, and thus belief allocated to a proposition 220:lead to the same result. This can be seen as 8: 434:{\displaystyle \sum _{A\in 2^{X}}m(A)=1\,\!} 2121:Non Standard Logics for Automated Reasoning 2059: 2057: 2055: 1973: 1971: 1969: 1967: 574:{\displaystyle 0\leq m(\emptyset )\leq 1.0} 150:giving rise to the name of the method: The 94:who proposed his approach as a response to 2253: 2140: 2034: 1991: 1945:Learn how and when to remove this message 1856: 1839: 1822: 1799: 1767: 1750: 1742: 1699: 1678: 1672: 1652: 1631: 1615: 1609: 1586: 1556: 1550: 1522: 1516: 1461: 1441: 1421: 1401: 1393: 1391: 1361: 1341: 1333: 1325: 1308: 1290: 1268: 1262: 1226: 1203: 1157: 1133: 1110: 1086: 1074: 1059:{\displaystyle H\in 2^{X}\neq \emptyset } 1044: 1032: 1008: 993: 969: 946: 931: 912: 890: 865: 852: 821: 815: 785: 779: 758: 752: 731: 725: 690: 666: 664: 644: 635: 623: 602: 590: 545: 525: 502: 482: 458: 452: 429: 403: 392: 386: 363: 339: 327: 304: 62:Learn how and when to remove this message 2119:Smets Ph. (1988) "Belief function". In: 1908:This article includes a list of general 2205:ECSQARU, Hammamet : Tunisie (2007) 1963: 685:, except the normalization that grants 368:{\displaystyle m:2^{X}\rightarrow \,\!} 2217:Integrated Computer-Aided Engineering 474:contains all possible subsets of the 44:providing more context for the reader 7: 1914:it lacks sufficient corresponding 1807: 1687: 1382:denote the atoms (also denoted as 1214: 1158: 1053: 1002: 774:are combined to a single function 698: 652:{\displaystyle A\in 2^{X}\cup {e}} 559: 527: 497:. In contrast to the DST the mass 264:are entertained and quantified by 110:. The underlying idea is that the 14: 1246:When a decision must be made the 205:is also possible but not likely ( 189:is also possible but not likely ( 2189:" In: Smets and Motro A. (eds.) 1899: 1538:. Such a function satisfies the 1487:principle of insufficient reason 23: 1509:pignistic probability functions 1500:most probably corresponds to a 713:{\displaystyle m(\emptyset )=0} 76:transferable belief model (TBM) 1861: 1853: 1844: 1836: 1827: 1819: 1810: 1804: 1772: 1764: 1755: 1747: 1690: 1684: 1643: 1637: 1583: 1571: 1568: 1531:{\displaystyle P_{\text{Bet}}} 1472: 1466: 1402: 1394: 1334: 1326: 1320: 1314: 1280: 1274: 1217: 1211: 1184: 1178: 1147: 1141: 1107: 1095: 1092: 943: 937: 924: 918: 880: 874: 871: 845: 839: 833: 701: 695: 683:Dempster's rule of combination 562: 556: 420: 414: 360: 348: 345: 218:Dempster's rule of combination 158:Zadeh’s example in TBM context 100:Dempster's rule of combination 1: 2271:The Transferable Belief Model 2175:10.1016/0004-3702(94)90026-4 2078:10.1016/0004-3702(94)90026-4 1491:principle of maximum entropy 1456:. Hence, probability masses 1511:are described by functions 2327: 2285:Software for TBM in Matlab 2255:10.1016/j.ijar.2007.03.004 2036:10.1016/j.ijar.2007.03.004 1066:is defined by a function: 618:, is in fact allocated to 611:{\displaystyle A\in 2^{X}} 533:{\displaystyle \emptyset } 298:probability mass function 152:transferable belief model 78:is an elaboration on the 1493:) according to which an 296:According to the DST, a 2236:Dempster, A.P. (2007). 2163:Artificial Intelligence 2066:Artificial Intelligence 2015:Dempster, A.P. (2007). 1929:more precise citations. 1252:pignistic probabilities 800:{\displaystyle m_{1,2}} 232:, they both agree that 2311:Dempster–Shafer theory 2229:10.3233/ICA-2007-14108 1890:Dempster–Shafer theory 1874: 1785: 1705: 1658: 1592: 1532: 1479: 1450: 1430: 1410: 1376: 1375:{\displaystyle x\in X} 1347: 1232: 1191: 1116: 1060: 1014: 952: 801: 768: 741: 714: 675: 653: 612: 575: 534: 511: 491: 468: 435: 369: 319:is defined such that: 313: 283:and are quantified by 80:Dempster–Shafer theory 2306:Statistical inference 1875: 1786: 1706: 1659: 1593: 1533: 1480: 1451: 1431: 1411: 1377: 1348: 1233: 1192: 1117: 1061: 1015: 953: 802: 769: 767:{\displaystyle m_{2}} 742: 740:{\displaystyle m_{1}} 715: 676: 654: 613: 576: 535: 512: 492: 469: 467:{\displaystyle 2^{X}} 436: 370: 314: 285:probability functions 104:open-world assumption 1798: 1741: 1671: 1608: 1549: 1515: 1502:uniform distribution 1478:{\displaystyle m(A)} 1460: 1440: 1420: 1416:the number of atoms 1390: 1360: 1261: 1202: 1132: 1073: 1031: 968: 814: 778: 751: 724: 689: 663: 622: 589: 544: 524: 501: 481: 476:frame of discernment 451: 385: 326: 303: 279:can be used to make 128:frame of discernment 2278:Publications on TBM 1717:introduced them as 1409:{\displaystyle |A|} 1250:are transferred to 674:{\displaystyle {e}} 142:with terms such as 40:improve the article 1870: 1781: 1733:Open world example 1701: 1654: 1626: 1588: 1540:probability axioms 1528: 1475: 1446: 1426: 1406: 1372: 1343: 1307: 1228: 1187: 1174: 1112: 1056: 1010: 948: 907: 797: 764: 737: 710: 671: 649: 608: 571: 530: 507: 487: 464: 431: 410: 365: 309: 248:The TBM describes 167:information fusion 140:probability update 136:probability masses 132:Bayesian inference 114:pertaining to the 1955: 1954: 1947: 1859: 1842: 1825: 1770: 1753: 1681: 1634: 1611: 1559: 1525: 1489:(also denoted as 1449:{\displaystyle A} 1429:{\displaystyle x} 1339: 1286: 1271: 1153: 886: 517:allocated to the 510:{\displaystyle m} 490:{\displaystyle X} 388: 312:{\displaystyle m} 244:Formal definition 144:degrees of belief 72: 71: 64: 2318: 2259: 2257: 2232: 2193:. Kluwer, Boston 2178: 2154: 2151:10.1109/34.55104 2144: 2107: 2097: 2091: 2088: 2082: 2081: 2061: 2050: 2047: 2041: 2040: 2038: 2012: 2006: 2005: 2002:10.1109/34.55104 1995: 1975: 1950: 1943: 1939: 1936: 1930: 1925:this article by 1916:inline citations 1903: 1902: 1895: 1879: 1877: 1876: 1871: 1860: 1857: 1843: 1840: 1826: 1823: 1790: 1788: 1787: 1782: 1771: 1768: 1754: 1751: 1710: 1708: 1707: 1702: 1683: 1682: 1679: 1663: 1661: 1660: 1655: 1636: 1635: 1632: 1625: 1597: 1595: 1594: 1589: 1561: 1560: 1557: 1537: 1535: 1534: 1529: 1527: 1526: 1523: 1484: 1482: 1481: 1476: 1455: 1453: 1452: 1447: 1435: 1433: 1432: 1427: 1415: 1413: 1412: 1407: 1405: 1397: 1381: 1379: 1378: 1373: 1352: 1350: 1349: 1344: 1340: 1338: 1337: 1329: 1323: 1309: 1306: 1273: 1272: 1269: 1237: 1235: 1234: 1229: 1196: 1194: 1193: 1188: 1173: 1121: 1119: 1118: 1113: 1091: 1090: 1065: 1063: 1062: 1057: 1049: 1048: 1027:in a hypothesis 1025:degree of belief 1019: 1017: 1016: 1011: 998: 997: 957: 955: 954: 949: 936: 935: 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1358: 1357: 1324: 1310: 1264: 1259: 1258: 1244: 1242:Pignistic level 1200: 1199: 1130: 1129: 1082: 1071: 1070: 1040: 1029: 1028: 1023:In the TBM the 989: 966: 965: 927: 908: 861: 848: 817: 812: 811: 781: 776: 775: 754: 749: 748: 727: 722: 721: 687: 686: 661: 660: 631: 620: 619: 598: 587: 586: 542: 541: 522: 521: 499: 498: 479: 478: 454: 449: 448: 399: 383: 382: 335: 324: 323: 301: 300: 294: 273:pignistic level 252:at two levels: 246: 238:Cromwell's rule 160: 96:Zadeh’s example 68: 57: 51: 48: 37: 28: 24: 17: 12: 11: 5: 2324: 2322: 2314: 2313: 2308: 2303: 2293: 2292: 2289: 2288: 2281: 2274: 2265: 2264:External links 2262: 2261: 2260: 2248:(2): 365–377. 2233: 2208: 2197: 2194: 2181:Smets Ph. and 2179: 2169:(2): 191–234. 2158: 2155: 2135:(5): 447–458. 2124: 2115: 2112: 2109: 2108: 2092: 2083: 2072:(2): 191–234. 2051: 2042: 2029:(2): 365–377. 2007: 1986:(5): 447–458. 1962: 1961: 1959: 1956: 1953: 1952: 1907: 1905: 1898: 1893: 1892: 1885: 1882: 1881: 1880: 1869: 1866: 1863: 1855: 1852: 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897: 894: 891: 887: 883: 877: 866: 862: 858: 853: 849: 842: 836: 828: 825: 822: 818: 810: 809: 808: 792: 789: 786: 782: 759: 755: 732: 728: 707: 704: 692: 684: 667: 645: 641: 636: 632: 628: 625: 603: 599: 595: 592: 584: 568: 565: 553: 550: 547: 520: 504: 484: 477: 459: 455: 447: 426: 423: 417: 411: 404: 400: 396: 393: 389: 381: 380: 379: 357: 354: 351: 340: 336: 332: 329: 322: 321: 320: 306: 299: 291: 286: 282: 278: 274: 270: 267: 263: 259: 255: 254: 253: 251: 243: 241: 239: 235: 231: 227: 223: 219: 215: 210: 208: 204: 201:= 0.95), but 200: 196: 192: 188: 185:= 0.95), but 184: 180: 176: 172: 168: 165:describes an 164: 157: 155: 153: 149: 145: 141: 137: 133: 129: 125: 121: 117: 113: 109: 108:normalization 105: 101: 97: 93: 89: 86:that a given 85: 81: 77: 66: 63: 55: 45: 41: 35: 32:This article 30: 21: 20: 2284: 2277: 2270: 2245: 2241: 2220: 2216: 2204: 2190: 2166: 2162: 2132: 2128: 2120: 2095: 2086: 2069: 2065: 2045: 2026: 2020: 2010: 1983: 1979: 1941: 1932: 1913: 1736: 1726: 1722: 1718: 1715:Philip Smets 1713: 1600: 1508: 1506: 1498:distribution 1494: 1355: 1247: 1245: 1124: 1024: 1022: 960: 443: 377: 295: 292:Credal level 276: 272: 261: 258:credal level 257: 247: 233: 229: 225: 213: 211: 206: 202: 198: 194: 190: 186: 182: 178: 174: 170: 161: 151: 147: 143: 139: 135: 126:outside the 75: 73: 58: 49: 38:Please help 33: 1927:introducing 1507:In the TBM 222:paradoxical 163:Lotfi Zadeh 88:proposition 84:probability 2295:Categories 2114:References 1910:references 1384:singletons 583:exhaustive 444:where the 124:hypothesis 2137:CiteSeerX 1988:CiteSeerX 1935:June 2010 1858:Head,Tail 1808:∅ 1719:pignistic 1688:∅ 1620:∈ 1613:∑ 1569:→ 1367:∈ 1301:⊆ 1295:∈ 1288:∑ 1215:∅ 1209:⁡ 1168:⊆ 1162:≠ 1159:∅ 1155:∑ 1139:⁡ 1093:→ 1054:∅ 1051:≠ 1038:∈ 1003:∅ 1000:≠ 987:∈ 895:∩ 888:∑ 859:⊗ 699:∅ 642:∪ 629:∈ 596:∈ 566:≤ 560:∅ 551:≤ 528:∅ 519:empty set 446:power set 397:∈ 390:∑ 346:→ 281:decisions 116:empty set 2185:(1995) " 2183:Kruse R. 1884:See also 148:transfer 98:against 52:May 2016 1923:improve 1495:unknown 277:beliefs 262:beliefs 250:beliefs 2139:  2103:  1990:  1912:, but 1727:belief 1386:) and 1356:where 961:where 659:where 275:where 260:where 120:belief 2301:Logic 1958:Notes 1601:with 1125:with 378:with 122:in a 2101:ISBN 1841:Tail 1824:Head 1769:Tail 1752:Head 1254:by: 807:by: 747:and 228:and 146:and 138:and 74:The 2250:doi 2225:doi 2171:doi 2147:doi 2074:doi 2031:doi 1998:doi 1680:Bet 1633:Bet 1558:Bet 1524:Bet 1270:Bet 1206:bel 1136:bel 1077:bel 569:1.0 177:or 42:by 2297:: 2246:48 2244:. 2240:. 2221:14 2219:. 2215:. 2203:, 2167:66 2165:. 2145:. 2133:12 2131:. 2070:66 2068:. 2054:^ 2027:48 2025:. 2019:. 1996:. 1984:12 1982:. 1966:^ 1868:1. 1851:Pr 1834:Pr 1817:Pr 1802:Pr 1762:Pr 1745:Pr 1729:. 1542:: 1504:. 1224:0. 271:a 256:a 216:. 173:, 154:. 2258:. 2252:: 2231:. 2227:: 2207:. 2177:. 2173:: 2153:. 2149:: 2080:. 2076:: 2039:. 2033:: 2004:. 2000:: 1948:) 1942:( 1937:) 1933:( 1919:. 1865:= 1862:) 1854:( 1848:+ 1845:) 1837:( 1831:+ 1828:) 1820:( 1814:+ 1811:) 1805:( 1779:1 1776:= 1773:) 1765:( 1759:+ 1756:) 1748:( 1697:0 1694:= 1691:) 1685:( 1676:P 1650:1 1647:= 1644:) 1641:x 1638:( 1629:P 1623:X 1617:x 1584:] 1581:1 1578:, 1575:0 1572:[ 1566:X 1563:: 1554:P 1520:P 1473:) 1470:A 1467:( 1464:m 1444:A 1424:x 1403:| 1399:A 1395:| 1370:X 1364:x 1335:| 1331:A 1327:| 1321:) 1318:A 1315:( 1312:m 1304:X 1298:A 1292:x 1284:= 1281:) 1278:x 1275:( 1266:P 1221:= 1218:) 1212:( 1185:) 1182:A 1179:( 1176:m 1171:H 1165:A 1151:= 1148:) 1145:H 1142:( 1108:] 1105:1 1102:, 1099:0 1096:[ 1088:X 1084:2 1080:: 1046:X 1042:2 1035:H 1006:. 995:X 991:2 984:C 981:, 978:B 975:, 972:A 944:) 941:C 938:( 933:2 929:m 925:) 922:B 919:( 914:1 910:m 904:A 901:= 898:C 892:B 884:= 881:) 878:A 875:( 872:) 867:2 863:m 854:1 850:m 846:( 843:= 840:) 837:A 834:( 829:2 826:, 823:1 819:m 793:2 790:, 787:1 783:m 760:2 756:m 733:1 729:m 708:0 705:= 702:) 696:( 693:m 668:e 646:e 637:X 633:2 626:A 604:X 600:2 593:A 563:) 557:( 554:m 548:0 505:m 485:X 460:X 456:2 427:1 424:= 421:) 418:A 415:( 412:m 405:X 401:2 394:A 361:] 358:1 355:, 352:0 349:[ 341:X 337:2 333:: 330:m 307:m 287:. 268:, 234:B 230:C 226:A 214:B 207:p 203:B 199:p 197:( 195:C 191:p 187:B 183:p 179:C 175:B 171:A 65:) 59:( 54:) 50:( 46:. 36:.

Index

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Dempster–Shafer theory
probability
proposition
Philippe Smets
Zadeh’s example
Dempster's rule of combination
open-world assumption
normalization
probability mass
empty set
belief
hypothesis
frame of discernment
Bayesian inference
Lotfi Zadeh
information fusion
Dempster's rule of combination
paradoxical
Cromwell's rule
beliefs
belief functions
decisions
probability functions
probability mass function
power set
frame of discernment
empty set

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