Knowledge (XXG)

Plate notation

Source đź“ť

28:. Instead of drawing each repeated variable individually, a plate or rectangle is used to group variables into a subgraph that repeat together, and a number is drawn on the plate to represent the number of repetitions of the subgraph in the plate. The assumptions are that the subgraph is duplicated that many times, the variables in the subgraph are indexed by the repetition number, and any links that cross a plate boundary are replicated once for each subgraph repetition. 357: 37: 403:
A number of extensions have been created by various authors to express more information than simply the conditional relationships. However, few of these have become standard. Perhaps the most commonly used extension is to use rectangles in place of circles to indicate non-random variables—either
443:
Categorical variables that act as "switches", and which pick one or more other random variables to condition on from a large set of such variables (e.g. mixture components), are indicated with a special type of arrow containing a squiggly line and ending in a T
367:
using plate notation. Smaller squares indicate fixed parameters; larger circles indicate random variables. Filled-in shapes indicate known values. The indication means a vector of size
95: 342: 312: 274: 206: 168: 240: 134: 526: 465: 434:
are similarly indicated by putting the matrix size in brackets in the middle of the node, with commas separating row size from column size.
361: 609: 48: 41: 604: 415:
The diagram on the right shows a few more non-standard conventions used in some articles in Knowledge (XXG) (e.g.
599: 395:— the value of this variable selects, for the other incoming variables, which value to use out of the size- 60: 518: 464:
drawing packages, but also as part of graphical user interfaces to Bayesian statistics programs such as
55:
that models how documents in a corpus are topically related. There are two variables not in any plate;
284:. The directed edges between variables indicate dependencies between the variables: for example, each 545: 437: 380: 109:
times, once for each document. The inner plate represents the variables associated with each of the
277: 569: 535: 73: 17: 469: 356: 561: 416: 36: 553: 317: 287: 249: 181: 143: 52: 218: 112: 70:
The outermost plate represents all the variables related to a specific document, including
431: 409: 281: 25: 549: 405: 593: 447:
Boldface is consistently used for vector or matrix nodes (but not categorical nodes).
424: 364: 573: 493: 412:), or variables whose values are computed deterministically from a random variable. 67:
is the parameter of the uniform Dirichlet prior on the per-topic word distribution.
440:
are indicated by placing their size (without a bracket) in the middle of the node.
246:. The circle representing the individual words is shaded, indicating that each 427:
are indicated by putting the vector size in brackets in the middle of the node.
565: 215:
in the corner represents the repetition of the variables in the inner plate
105:
in the corner of the plate indicates that the variables inside are repeated
280:, and the other circles are empty, indicating that the other variables are 557: 540: 461: 355: 35: 473: 457: 24:
is a method of representing variables that repeat in a
320: 290: 252: 221: 184: 146: 115: 76: 63:prior on the per-document topic distributions, and 336: 306: 268: 234: 200: 162: 128: 89: 519:"Operations for Learning with Graphical Models" 456:Plate notation has been implemented in various 8: 527:Journal of Artificial Intelligence Research 539: 408:given a fixed value (or computed through 387:outcomes. The squiggly line coming from 325: 319: 295: 289: 257: 251: 226: 220: 189: 183: 151: 145: 120: 114: 81: 75: 484: 242:times, once for each word in document 97:, the topic distribution for document 7: 517:Buntine, Wray L. (December 1994). 492:Ghahramani, Zoubin (August 2007). 170:is the topic distribution for the 14: 534:. AI Access Foundation: 159–225. 391:ending in a crossbar indicates a 59:is the parameter of the uniform 1: 47:In this example, we consider 430:Variables that are actually 423:Variables that are actually 498:(Speech). TĂĽbingen, Germany 404:parameters to be computed, 90:{\displaystyle \theta _{i}} 49:Latent Dirichlet allocation 42:Latent Dirichlet allocation 626: 371:; means a matrix of size 399:array of possible values. 208:is the actual word used. 452:Software implementation 400: 338: 337:{\displaystyle z_{ij}} 308: 307:{\displaystyle w_{ij}} 270: 269:{\displaystyle w_{ij}} 236: 202: 201:{\displaystyle w_{ij}} 164: 163:{\displaystyle z_{ij}} 130: 91: 44: 610:Mathematical notation 438:Categorical variables 362:multivariate Gaussian 359: 339: 309: 271: 237: 235:{\displaystyle N_{j}} 203: 165: 131: 129:{\displaystyle N_{i}} 92: 39: 381:categorical variable 318: 288: 250: 219: 182: 174:th word in document 144: 113: 74: 550:1994cs.......12102B 40:Plate notation for 401: 379:; K alone means a 334: 304: 266: 232: 198: 160: 136:words in document 126: 87: 45: 18:Bayesian inference 605:Bayesian networks 417:variational Bayes 617: 600:Graphical models 585: 584: 582: 580: 543: 523: 514: 508: 507: 505: 503: 495:Graphical models 489: 343: 341: 340: 335: 333: 332: 313: 311: 310: 305: 303: 302: 282:latent variables 275: 273: 272: 267: 265: 264: 241: 239: 238: 233: 231: 230: 207: 205: 204: 199: 197: 196: 169: 167: 166: 161: 159: 158: 135: 133: 132: 127: 125: 124: 96: 94: 93: 88: 86: 85: 53:Bayesian network 625: 624: 620: 619: 618: 616: 615: 614: 590: 589: 588: 578: 576: 558:10.1613/jair.62 521: 516: 515: 511: 501: 499: 491: 490: 486: 482: 454: 432:random matrices 410:empirical Bayes 406:hyperparameters 354: 321: 316: 315: 291: 286: 285: 253: 248: 247: 222: 217: 216: 185: 180: 179: 147: 142: 141: 116: 111: 110: 77: 72: 71: 34: 26:graphical model 12: 11: 5: 623: 621: 613: 612: 607: 602: 592: 591: 587: 586: 509: 483: 481: 478: 453: 450: 449: 448: 445: 441: 435: 428: 425:random vectors 353: 350: 331: 328: 324: 301: 298: 294: 263: 260: 256: 229: 225: 195: 192: 188: 157: 154: 150: 123: 119: 84: 80: 33: 30: 22:plate notation 13: 10: 9: 6: 4: 3: 2: 622: 611: 608: 606: 603: 601: 598: 597: 595: 575: 571: 567: 563: 559: 555: 551: 547: 542: 537: 533: 529: 528: 520: 513: 510: 497: 496: 488: 485: 479: 477: 475: 471: 467: 463: 459: 451: 446: 442: 439: 436: 433: 429: 426: 422: 421: 420: 418: 413: 411: 407: 398: 394: 390: 386: 382: 378: 374: 370: 366: 365:mixture model 363: 358: 351: 349: 347: 329: 326: 322: 299: 296: 292: 283: 279: 261: 258: 254: 245: 227: 223: 214: 209: 193: 190: 186: 177: 173: 155: 152: 148: 139: 121: 117: 108: 104: 100: 82: 78: 68: 66: 62: 58: 54: 50: 43: 38: 31: 29: 27: 23: 19: 577:. Retrieved 531: 525: 512: 500:. Retrieved 494: 487: 455: 414: 402: 396: 392: 388: 384: 376: 372: 368: 345: 243: 212: 210: 175: 171: 137: 106: 102: 98: 69: 64: 56: 46: 21: 15: 579:21 February 502:21 February 314:depends on 594:Categories 541:cs/9412102 480:References 470:BayesiaLab 352:Extensions 278:observable 566:1076-9757 444:junction. 360:Bayesian 79:θ 61:Dirichlet 574:11672931 546:Bibcode 101:. The 32:Example 572:  564:  393:switch 375:× 178:, and 570:S2CID 536:arXiv 522:(PDF) 462:LaTeX 383:with 581:2008 562:ISSN 504:2008 474:PyMC 472:and 468:and 466:BUGS 344:and 211:The 51:, a 554:doi 458:TeX 419:): 276:is 16:In 596:: 568:. 560:. 552:. 544:. 530:. 524:. 476:. 348:. 140:: 20:, 583:. 556:: 548:: 538:: 532:2 506:. 460:/ 397:K 389:z 385:K 377:D 373:D 369:K 346:β 330:j 327:i 323:z 300:j 297:i 293:w 262:j 259:i 255:w 244:i 228:j 224:N 213:N 194:j 191:i 187:w 176:i 172:j 156:j 153:i 149:z 138:i 122:i 118:N 107:M 103:M 99:i 83:i 65:β 57:α

Index

Bayesian inference
graphical model

Latent Dirichlet allocation
Latent Dirichlet allocation
Bayesian network
Dirichlet
observable
latent variables

multivariate Gaussian
mixture model
categorical variable
hyperparameters
empirical Bayes
variational Bayes
random vectors
random matrices
Categorical variables
TeX
LaTeX
BUGS
BayesiaLab
PyMC
Graphical models
"Operations for Learning with Graphical Models"
Journal of Artificial Intelligence Research
arXiv
cs/9412102
Bibcode

Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.

↑