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Kolmogorov's zero–one law

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is a tail event. Thus by Kolmogorov 0-1 law, it has either probability 0 or 1 to happen. Note that independence is required for the tail event condition to hold. Without independence we can consider a sequence that's either
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Tail events are defined in terms of countably infinite families of σ-algebras. For illustrative purposes, we present here the special case in which each sigma algebra is generated by a random variable
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Curriculum Vitae and Biography. Kolmogorov School. Ph.D. students and descendants of A. N. Kolmogorov. A. N. Kolmogorov works, books, papers, articles. Photographs and Portraits of A. N. Kolmogorov.
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In many situations, it can be easy to apply Kolmogorov's zero–one law to show that some event has probability 0 or 1, but surprisingly hard to determine
577: 295:, but the latter condition is strictly weaker and does not suffice to prove the zero-one law.) For example, the event that the sequence of the 504:
Tail events are precisely those events whose occurrence can still be determined if an arbitrarily large but finite initial segment of the
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A more general statement of Kolmogorov's zero–one law holds for sequences of independent σ-algebras. Let (Ω,
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The statement of the law in terms of random variables is obtained from the latter by taking each
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are, for example, all Bernoulli-distributed, then the event that there are infinitely many
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converges, and the event that its sum converges are both tail events. If the
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be a sequence of independent random variables, then the event
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to be the σ-algebra generated by the random variable
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Special case in probability theory; introduces tail events
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of each finite subset of these random variables. (Note:
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each. In this case the sum converges with probability
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be the sigma-algebra generated jointly by all of the
122: 94: 67: 1338: 907:, but which is independent of any finite number of 794:are stochastically independent, then for any event 1260: 1233: 1206: 1162: 1117: 1038: 960: 853: 774: 644: 523: 493: 473: 446: 369: 341: 314: 287: 260: 240: 216: 192: 159: 132: 108: 80: 1339:Brzezniak, Zdzislaw; Zastawniak, Thomasz (2000). 637: 599: 53:happen or almost surely not happen; that is, the 1403: 1059: 538:of these two extreme values is the correct one. 447:{\displaystyle X_{k}=X_{k+1}=\dots =X_{k+100}=1} 785:Kolmogorov's zero–one law asserts that, if the 567:be a sequence of σ-algebras contained in 1027: 1013: 268:is uniquely determined by the values of the 1396:The Legacy of Andrei Nikolaevich Kolmogorov 1366:A first look at rigorous probability theory 57:of such an event occurring is zero or one. 655:be the smallest σ-algebra containing 1361: 842: 729: 363: 102: 1314: 1404: 995:are Kolmogorov automorphisms but not 1317:Probability theory: An analytic view 987:that obeys the 0-1 law is called a 193:{\displaystyle F\in {\mathcal {F}}} 38:, specifies that a certain type of 13: 1069: 941: 809: 757: 696: 620: 233: 185: 125: 14: 1428: 1389: 981:measure-preserving transformation 370:{\displaystyle k\in \mathbb {N} } 109:{\displaystyle k\in \mathbb {N} } 1362:Rosenthal, Jeffrey S. (2006). 1261:{\displaystyle {\frac {1}{2}}} 1234:{\displaystyle {\frac {1}{2}}} 1207:{\displaystyle (1,1,1,\dots )} 1201: 1177: 1163:{\displaystyle (0,0,0,\dots )} 1157: 1133: 1066: 848: 831: 817: 814: 735: 718: 704: 701: 541: 241:{\displaystyle {\mathcal {F}}} 133:{\displaystyle {\mathcal {F}}} 1: 1308: 1039:{\displaystyle \{X_{n}\}_{n}} 202:probabilistically independent 36:Andrey Nikolaevich Kolmogorov 7: 1271: 971: 248:implies that membership in 10: 1433: 1341:Basic Stochastic Processes 1321:Cambridge University Press 1283:Hewitt–Savage zero–one law 985:standard probability space 481:models the outcome of the 44:tail event of independent 18: 454:is a tail event. If each 32:Kolmogorov's zero–one law 1315:Stroock, Daniel (1999). 1003:also obeys the 0-1 law. 993:Bernoulli automorphisms 989:Kolmogorov automorphism 676:terminal σ-algebra 1262: 1235: 1208: 1164: 1119: 1094: 1040: 962: 945: 855: 776: 761: 646: 624: 525: 495: 475: 448: 371: 343: 316: 289: 262: 242: 218: 194: 161: 134: 110: 82: 1263: 1236: 1209: 1165: 1120: 1074: 1041: 963: 925: 856: 777: 741: 647: 604: 526: 524:{\displaystyle X_{k}} 496: 476: 474:{\displaystyle X_{k}} 449: 372: 344: 342:{\displaystyle X_{k}} 317: 315:{\displaystyle X_{k}} 290: 288:{\displaystyle X_{k}} 263: 243: 219: 200:is an event which is 195: 162: 160:{\displaystyle X_{k}} 135: 111: 83: 81:{\displaystyle X_{k}} 1412:Probability theorems 1319:(revised ed.). 1278:Borel–Cantelli lemma 1245: 1218: 1174: 1130: 1050: 1010: 920: 798: 691: 578: 508: 485: 458: 381: 353: 326: 299: 272: 252: 228: 208: 174: 144: 120: 92: 65: 34:, named in honor of 1288:Lévy's zero–one law 1293:Tail sigma-algebra 1258: 1231: 1204: 1160: 1115: 1107: exists  1073: 1036: 1001:percolation theory 958: 957: 851: 772: 642: 521: 491: 471: 444: 367: 339: 312: 285: 258: 238: 214: 190: 157: 130: 106: 78: 28:probability theory 1381:978-981-270-371-2 1330:978-0-521-66349-6 1256: 1229: 1214:with probability 1108: 1058: 861:, one has either 556:probability space 494:{\displaystyle k} 261:{\displaystyle F} 217:{\displaystyle F} 1424: 1385: 1369: 1358: 1334: 1267: 1265: 1264: 1259: 1257: 1249: 1240: 1238: 1237: 1232: 1230: 1222: 1213: 1211: 1210: 1205: 1169: 1167: 1166: 1161: 1124: 1122: 1121: 1116: 1114: 1110: 1109: 1106: 1104: 1103: 1093: 1088: 1072: 1045: 1043: 1042: 1037: 1035: 1034: 1025: 1024: 967: 965: 964: 959: 956: 955: 954: 944: 939: 860: 858: 857: 852: 847: 846: 845: 829: 828: 813: 812: 781: 779: 778: 773: 771: 770: 760: 755: 734: 733: 732: 716: 715: 700: 699: 651: 649: 648: 643: 641: 640: 634: 633: 623: 618: 603: 602: 590: 589: 530: 528: 527: 522: 520: 519: 500: 498: 497: 492: 480: 478: 477: 472: 470: 469: 453: 451: 450: 445: 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as 682: 668: 659: 653: 652: 639: 632: 628: 622: 617: 614: 611: 607: 601: 596: 593: 588: 584: 562: 543: 540: 518: 514: 490: 468: 464: 443: 440: 435: 432: 429: 425: 421: 418: 415: 410: 407: 404: 400: 396: 391: 387: 365: 361: 358: 336: 332: 309: 305: 282: 278: 257: 235: 213: 187: 182: 179: 154: 150: 127: 104: 100: 97: 75: 71: 49:, will either 15: 9: 6: 4: 3: 2: 1429: 1418: 1415: 1413: 1410: 1409: 1407: 1397: 1394: 1393: 1383: 1377: 1373: 1368: 1367: 1360: 1356: 1354:3-540-76175-6 1350: 1346: 1342: 1337: 1332: 1326: 1322: 1318: 1313: 1312: 1304: 1301: 1299: 1296: 1294: 1291: 1289: 1286: 1284: 1281: 1279: 1276: 1275: 1269: 1253: 1250: 1226: 1223: 1198: 1195: 1192: 1189: 1186: 1183: 1180: 1154: 1151: 1148: 1145: 1142: 1139: 1136: 1111: 1100: 1096: 1090: 1085: 1082: 1079: 1075: 1063: 1054: 1031: 1021: 1017: 1004: 1002: 998: 994: 990: 986: 982: 979: 969: 951: 947: 936: 933: 930: 926: 914: 910: 905: 901: 896: 892: 887: 883: 878: 876: 872: 868: 864: 838: 835: 825: 821: 804: 801: 792: 788: 783: 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The 542:Formulation 55:probability 42:, namely a 1406:Categories 1309:References 997:vice versa 978:invertible 377:such that 169:tail event 167:. Then, a 1303:Tail risk 1298:Long tail 1199:… 1155:… 1076:∑ 1070:∞ 1067:→ 942:∞ 927:⋂ 869:) = 0 or 839:∈ 805:∈ 758:∞ 743:⋂ 726:∈ 621:∞ 606:⋃ 595:σ 417:⋯ 360:∈ 181:∈ 99:∈ 1345:Springer 1272:See also 972:Examples 558:and let 21:fat tail 678:of the 554:) be a 1378:  1351:  1327:  991:. All 571:. Let 116:. Let 983:on a 877:)=1. 536:which 40:event 1376:ISBN 1349:ISBN 1325:ISBN 1006:Let 88:for 1170:or 1060:lim 976:An 434:100 26:In 1408:: 1374:. 1372:37 1347:. 1343:. 1323:. 1268:. 968:. 782:. 672:+1 664:, 30:, 1384:. 1357:. 1335:. 1333:. 1254:2 1251:1 1227:2 1224:1 1202:) 1196:, 1193:1 1190:, 1187:1 1184:, 1181:1 1178:( 1158:) 1152:, 1149:0 1146:, 1143:0 1140:, 1137:0 1134:( 1112:} 1101:k 1097:X 1091:n 1086:1 1083:= 1080:k 1064:n 1055:{ 1032:n 1028:} 1022:n 1018:X 1014:{ 952:n 948:G 937:1 934:= 931:n 913:n 909:X 904:n 900:X 895:n 891:X 886:n 882:F 875:E 873:( 871:P 867:E 865:( 863:P 849:) 843:N 836:n 832:) 826:n 822:F 818:( 815:( 810:T 802:E 791:n 787:F 768:n 764:G 753:1 750:= 747:n 739:= 736:) 730:N 723:n 719:) 713:n 709:F 705:( 702:( 697:T 684:n 680:F 670:n 666:F 661:n 657:F 638:) 631:k 627:F 616:n 613:= 610:k 600:( 592:= 587:n 583:G 569:F 564:n 560:F 552:P 550:, 548:F 517:k 513:X 489:k 467:k 463:X 442:1 439:= 431:+ 428:k 424:X 420:= 414:= 409:1 406:+ 403:k 399:X 395:= 390:k 386:X 364:N 357:k 335:k 331:X 308:k 304:X 281:k 277:X 256:F 234:F 212:F 186:F 178:F 153:k 149:X 126:F 103:N 96:k 74:k 70:X 23:.

Index

fat tail
probability theory
Andrey Nikolaevich Kolmogorov
event
σ-algebras
almost surely
probability
probabilistically independent
probability space
invertible
measure-preserving transformation
standard probability space
Kolmogorov automorphism
Bernoulli automorphisms
percolation theory
Borel–Cantelli lemma
Hewitt–Savage zero–one law
Lévy's zero–one law
Tail sigma-algebra
Long tail
Tail risk
Cambridge University Press
ISBN
978-0-521-66349-6
Springer
ISBN
3-540-76175-6
A first look at rigorous probability theory
37
ISBN

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