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Bayesian inference using Gibbs sampling

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The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries
68: 75: 504: 281: 199: 108: 57: 163:. MultiBUGS is built on the existing algorithms and tools in OpenBUGS and WinBUGS, which are no longer developed, and implements 204: 46: 478: 82: 183: 35: 514: 471: 168: 137: 145: 273: 443: 129: 377:"MultiBUGS: A Parallel Implementation of the BUGS Modeling Framework for Faster Bayesian Inference" 141: 357: 318: 164: 133: 408: 298: 277: 250: 455: 398: 388: 349: 310: 240: 187: 403: 376: 375:
Goudie, Robert J. B.; Turner, Rebecca M.; De Angelis, Daniela; Thomas, Andrew (2020).
498: 361: 337: 338:"WinBUGS—A Bayesian modelling framework: concepts, structure, and extensibility" 24: 429: 451: 353: 412: 254: 172: 393: 336:
Lunn, David J.; Thomas, Andrew; Best, Nicky; Spiegelhalter, David (2000).
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Lunn, David; Spiegelhalter, David; Thomas, Andrew; Best, Nicky (2009).
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The BUGS project has evolved through four main versions: ClassicBUGS,
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Alternative implementations of the BUGS language include
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Gilks, W. R.; Thomas, A.; Spiegelhalter, D. J. (1994).
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at the Medical Research Council Biostatistics Unit in
49:. Unsourced material may be challenged and removed. 148:in 1989 and released as free software in 1991. 479: 8: 16:Statistical software for Bayesian inference 486: 472: 402: 392: 244: 175:acts as an interface to MultiBUGS, while 109:Learn how and when to remove this message 58:"Bayesian inference using Gibbs sampling" 216: 122:Bayesian inference using Gibbs sampling 179:is an extension of the BUGS language. 510:Domain-specific programming languages 7: 440: 438: 222: 220: 140:(MCMC) methods. It was developed by 47:adding citations to reliable sources 458:. You can help Knowledge (XXG) by 14: 268:McGrayne, Sharon Bertsch (2012). 200:Spike and slab variable selection 167:to speed up computation. Several 442: 23: 381:Journal of Statistical Software 205:Bayesian structural time series 34:needs additional citations for 1: 531: 437: 505:Computational statistics 342:Statistics and Computing 171:packages are available, 138:Markov chain Monte Carlo 354:10.1023/A:1008929526011 454:-related article is a 233:Statistics in Medicine 394:10.18637/jss.v095.i07 274:Yale University Press 130:statistical software 43:improve this article 142:David Spiegelhalter 134:Bayesian inference 467: 466: 239:(25): 3049–3067. 119: 118: 111: 93: 522: 515:Statistics stubs 488: 481: 474: 446: 439: 430:The BUGS Project 417: 416: 406: 396: 372: 366: 365: 333: 327: 326: 303:The Statistician 294: 288: 287: 265: 259: 258: 248: 246:10.1002/sim.3680 224: 114: 107: 103: 100: 94: 92: 51: 27: 19: 530: 529: 525: 524: 523: 521: 520: 519: 495: 494: 493: 492: 435: 426: 421: 420: 374: 373: 369: 335: 334: 330: 315:10.2307/2348941 296: 295: 291: 284: 276:. p. 226. 267: 266: 262: 226: 225: 218: 213: 196: 165:parallelization 132:for performing 115: 104: 98: 95: 52: 50: 40: 28: 17: 12: 11: 5: 528: 526: 518: 517: 512: 507: 497: 496: 491: 490: 483: 476: 468: 465: 464: 447: 433: 432: 425: 424:External links 422: 419: 418: 367: 348:(4): 325–337. 328: 309:(1): 169–177. 289: 282: 260: 215: 214: 212: 209: 208: 207: 202: 195: 192: 117: 116: 31: 29: 22: 15: 13: 10: 9: 6: 4: 3: 2: 527: 516: 513: 511: 508: 506: 503: 502: 500: 489: 484: 482: 477: 475: 470: 469: 463: 461: 457: 453: 448: 445: 441: 436: 431: 428: 427: 423: 414: 410: 405: 400: 395: 390: 386: 382: 378: 371: 368: 363: 359: 355: 351: 347: 343: 339: 332: 329: 324: 320: 316: 312: 308: 304: 300: 293: 290: 285: 283:9780300188226 279: 275: 271: 264: 261: 256: 252: 247: 242: 238: 234: 230: 223: 221: 217: 210: 206: 203: 201: 198: 197: 193: 191: 189: 185: 180: 178: 174: 170: 166: 162: 158: 154: 149: 147: 143: 139: 135: 131: 127: 123: 113: 110: 102: 91: 88: 84: 81: 77: 74: 70: 67: 63: 60: –  59: 55: 54:Find sources: 48: 44: 38: 37: 32:This article 30: 26: 21: 20: 460:expanding it 449: 434: 384: 380: 370: 345: 341: 331: 306: 302: 292: 269: 263: 236: 232: 181: 150: 125: 121: 120: 105: 96: 86: 79: 72: 65: 53: 41:Please help 36:verification 33: 387:(7): 1–20. 173:R2MultiBUGS 499:Categories 452:statistics 211:References 99:March 2013 69:newspapers 161:MultiBUGS 146:Cambridge 413:33071678 255:19630097 194:See also 157:OpenBUGS 404:7116196 362:2722195 323:2348941 153:WinBUGS 128:) is a 83:scholar 411:  401:  360:  321:  280:  253:  177:Nimble 136:using 85:  78:  71:  64:  56:  450:This 358:S2CID 319:JSTOR 90:JSTOR 76:books 456:stub 409:PMID 278:ISBN 251:PMID 188:Stan 186:and 184:JAGS 159:and 126:BUGS 62:news 399:PMC 389:doi 350:doi 311:doi 241:doi 45:by 501:: 407:. 397:. 385:95 383:. 379:. 356:. 346:10 344:. 340:. 317:. 307:43 305:. 301:. 272:. 249:. 237:28 235:. 231:. 219:^ 190:. 155:, 487:e 480:t 473:v 462:. 415:. 391:: 364:. 352:: 325:. 313:: 286:. 257:. 243:: 169:R 124:( 112:) 106:( 101:) 97:( 87:· 80:· 73:· 66:· 39:.

Index


verification
improve this article
adding citations to reliable sources
"Bayesian inference using Gibbs sampling"
news
newspapers
books
scholar
JSTOR
Learn how and when to remove this message
statistical software
Bayesian inference
Markov chain Monte Carlo
David Spiegelhalter
Cambridge
WinBUGS
OpenBUGS
MultiBUGS
parallelization
R
R2MultiBUGS
Nimble
JAGS
Stan
Spike and slab variable selection
Bayesian structural time series


"The BUGS project: Evolution, critique and future directions"

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