Information Theory, Inference, and Learning ... - Inference Group

More documents

Recommendations

Info

Copyright Cambridge University Press 2003. On-screen viewing permitted. Printing not permitted. http://www.cambridge.org/0521642981You can buy this book for 30 pounds or $50. See http://www.inference.phy.cam.ac.uk/mackay/itila/ for links.614 BibliographyBretthorst, G. (1988) Bayesian Spectrum Analysis and ParameterEstimation. Springer. Also available at bayes.wustl.edu.Bridle, J. S. (1989) Probabilistic interpretation of feedforwardclassification network outputs, with relationships to statisticalpattern recognition. In Neuro-computing: Algorithms, Architecturesand Applications, ed. by F. Fougelman-Soulie andJ. Hérault. Springer–Verlag.Bulmer, M. (1985) The Mathematical Theory of QuantitativeGenetics. Oxford Univ. Press.Burrows, M., and Wheeler, D. J. (1994) A block-sorting losslessdata compression algorithm. Technical Report 124, DigitalSRC.Byers, J., Luby, M., Mitzenmacher, M., and Rege, A. (1998)A digital fountain approach to reliable distribution of bulk data.In Proc. ACM SIGCOMM ’98, September 2–4, 1998 .Cairns-Smith, A. G. (1985) Seven Clues to the Origin of Life.Cambridge Univ. Press.Calderbank, A. R., and Shor, P. W. (1996) Good quantumerror-correcting codes exist. Phys. Rev. A 54: 1098. quant-ph/9512032.Carroll, L. (1998) Alice’s Adventures in Wonderland; and,Through the Looking-glass: and what Alice Found There.Macmillan Children’s Books.Childs, A. M., Patterson, R. B., and MacKay, D. J. C.(2001) Exact sampling from non-attractive distributions usingsummary states. Physical Review E 63: 036113.Chu, W., Keerthi, S. S., and Ong, C. J. (2001) A unifiedloss function in Bayesian framework for support vector regression.In Proc. 18th International Conf. on Machine Learning,pp. 51–58.Chu, W., Keerthi, S. S., and Ong, C. J. (2002) A new Bayesiandesign method for support vector classification. In Special Sectionon Support Vector Machines of the 9th International Conf.on Neural Information Processing.Chu, W., Keerthi, S. S., and Ong, C. J. (2003a) Bayesiansupport vector regression using a unified loss function. IEEETrans. on Neural Networks. Submitted.Chu, W., Keerthi, S. S., and Ong, C. J. (2003b) Bayesiantrigonometric support vector classifier. Neural Computation.Chung, S.-Y., Richardson, T. J., and Urbanke, R. L. (2001)Analysis of sum-product decoding of low-density parity-checkcodes using a Gaussian approximation. IEEE Trans. Info. Theory47 (2): 657–670.Chung, S.-Y., Urbanke, R. L., and Richardson, T. J.,(1999) LDPC code design applet. lids.mit.edu/~sychung/gaopt.html.Comon, P., Jutten, C., and Herault, J. (1991) Blind separationof sources. 2. Problems statement. Signal Processing 24(1): 11–20.Copas, J. B. (1983) Regression, prediction and shrinkage (withdiscussion). J. R. Statist. Soc. B 45 (3): 311–354.Cover, T. M. (1965) Geometrical and statistical properties ofsystems of linear inequalities with applications in pattern recognition.IEEE Trans. on Electronic Computers 14: 326–334.Cover, T. M., and Thomas, J. A. (1991) Elements of InformationTheory. Wiley.Cowles, M. K., and Carlin, B. P. (1996) Markov-chain Monte-Carlo convergence diagnostics – a comparative review. J. AmericanStatistical Assoc. 91 (434): 883–904.Cox, R. (1946) Probability, frequency, and reasonable expectation.Am. J. Physics 14: 1–13.Cressie, N. (1993) Statistics for Spatial Data. Wiley.Davey, M. C. (1999) Error-correction using Low-Density Parity-Check Codes. Univ. of Cambridge PhD dissertation.Davey, M. C., and MacKay, D. J. C. (1998) Low density paritycheck codes over GF(q). IEEE Communications Letters 2(6): 165–167.Davey, M. C., and MacKay, D. J. C. (2000) Watermark codes:Reliable communication over insertion/deletion channels. InProc. 2000 IEEE International Symposium on Info. Theory,p. 477.Davey, M. C., and MacKay, D. J. C. (2001) Reliable communicationover channels with insertions, deletions and substitutions.IEEE Trans. Info. Theory 47 (2): 687–698.Dawid, A., Stone, M., and Zidek, J. (1996) Critique ofE.T Jaynes’s ‘paradoxes of probability theory’. Technical Report172, Dept. of Statistical Science, Univ. College London.Dayan, P., Hinton, G. E., Neal, R. M., and Zemel, R. S.(1995) The Helmholtz machine. Neural Computation 7(5): 889–904.Divsalar, D., Jin, H., and McEliece, R. J. (1998) Coding theoremsfor ‘turbo-like’ codes. In Proc. 36th Allerton Conf. onCommunication, Control, and Computing, Sept. 1998 , pp. 201–210. Allerton House.Doucet, A., de Freitas, J., and Gordon, N. eds. (2001) SequentialMonte Carlo Methods in Practice. Springer.Duane, S., Kennedy, A. D., Pendleton, B. J., and Roweth,D. (1987) Hybrid Monte Carlo. Physics Letters B 195: 216–222.Durbin, R., Eddy, S. R., Krogh, A., and Mitchison, G. (1998)Biological Sequence Analysis. Probabilistic Models of Proteinsand Nucleic Acids. Cambridge Univ. Press.Dyson, F. J. (1985) Origins of Life. Cambridge Univ. Press.Elias, P. (1975) Universal codeword sets and representations ofthe integers. IEEE Trans. Info. Theory 21 (2): 194–203.Eyre-Walker, A., and Keightley, P. (1999) High genomicdeleterious mutation rates in hominids. Nature 397: 344–347.Felsenstein, J. (1985) Recombination and sex: is MaynardSmith necessary? In Evolution. Essays in Honour of JohnMaynard Smith, ed. by P. J. Greenwood, P. H. Harvey, andM. Slatkin, pp. 209–220. Cambridge Univ. Press.Ferreira, H., Clarke, W., Helberg, A., Abdel-Ghaffar,K. S., and Vinck, A. H. (1997) Insertion/deletion correctionwith spectral nulls. IEEE Trans. Info. Theory 43 (2): 722–732.Feynman, R. P. (1972) Statistical Mechanics. Addison–Wesley.Forney, Jr., G. D. (1966) Concatenated Codes. MIT Press.Forney, Jr., G. D. (2001) Codes on graphs: Normal realizations.IEEE Trans. Info. Theory 47 (2): 520–548.Frey, B. J. (1998) Graphical Models for Machine Learning andDigital Communication. MIT Press.Gallager, R. G. (1962) Low density parity check codes. IRETrans. Info. Theory IT-8: 21–28.Gallager, R. G. (1963) Low Density Parity Check Codes. Number21 in MIT Research monograph series. MIT Press. Availablefrom www.inference.phy.cam.ac.uk/mackay/gallager/papers/.Gallager, R. G. (1968) Information Theory and Reliable Communication.Wiley.Gallager, R. G. (1978) Variations on a theme by Huffman.IEEE Trans. Info. Theory IT-24 (6): 668–674.Gibbs, M. N. (1997) Bayesian Gaussian Processes for Regressionand Classification. Cambridge Univ. PhD dissertation.www.inference.phy.cam.ac.uk/mng10/.Gibbs, M. N., and MacKay, D. J. C., (1996) Efficientimplementation of Gaussian processes for interpolation.www.inference.phy.cam.ac.uk/mackay/abstracts/gpros.html.Gibbs, M. N., and MacKay, D. J. C. (2000) Variational Gaussianprocess classifiers. IEEE Trans. on Neural Networks 11(6): 1458–1464.Gilks, W., Roberts, G., and George, E. (1994) Adaptivedirection sampling. Statistician 43: 179–189.
Copyright Cambridge University Press 2003. On-screen viewing permitted. Printing not permitted. http://www.cambridge.org/0521642981You can buy this book for 30 pounds or $50. See http://www.inference.phy.cam.ac.uk/mackay/itila/ for links.Bibliography 615Gilks, W., and Wild, P. (1992) Adaptive rejection sampling forGibbs sampling. Applied Statistics 41: 337–348.Gilks, W. R., Richardson, S., and Spiegelhalter, D. J.(1996) Markov Chain Monte Carlo in Practice. Chapman andHall.Goldie, C. M., and Pinch, R. G. E. (1991) Communicationtheory. Cambridge Univ. Press.Golomb, S. W., Peile, R. E., and Scholtz, R. A. (1994) BasicConcepts in Information Theory and Coding: The Adventuresof Secret Agent 00111 . Plenum Press.Good, I. J. (1979) Studies in the history of probability and statistics.XXXVII. A.M. Turing’s statistical work in World War II.Biometrika 66 (2): 393–396.Graham, R. L. (1966) On partitions of a finite set. Journal ofCombinatorial Theory 1: 215–223.Graham, R. L., and Knowlton, K. C., (1968) Method of identifyingconductors in a cable by establishing conductor connectiongroupings at both ends of the cable. U.S. Patent 3,369,177.Green, P. J. (1995) Reversible jump Markov chain Monte Carlocomputation and Bayesian model determination. Biometrika82: 711–732.Gregory, P. C., and Loredo, T. J. (1992) A new method forthe detection of a periodic signal of unknown shape and period.In Maximum Entropy and Bayesian Methods,, ed. by G. Ericksonand C. Smith. Kluwer. Also in Astrophysical Journal, 398,pp. 146–168, Oct 10, 1992.Gull, S. F. (1988) Bayesian inductive inference and maximumentropy. In Maximum Entropy and Bayesian Methods in Scienceand Engineering, vol. 1: Foundations, ed. by G. Ericksonand C. Smith, pp. 53–74. Kluwer.Gull, S. F. (1989) Developments in maximum entropy data analysis.In Maximum Entropy and Bayesian Methods, Cambridge1988 , ed. by J. Skilling, pp. 53–71. Kluwer.Gull, S. F., and Daniell, G. (1978) Image reconstruction fromincomplete and noisy data. Nature 272: 686–690.Hamilton, W. D. (2002) Narrow Roads of Gene Land, Volume2: Evolution of Sex. Oxford Univ. Press.Hanson, R., Stutz, J., and Cheeseman, P. (1991a) Bayesianclassification theory. Technical Report FIA–90-12-7-01, NASAAmes.Hanson, R., Stutz, J., and Cheeseman, P. (1991b) Bayesianclassification with correlation and inheritance. In Proc. 12th Intern.Joint Conf. on Artificial Intelligence, Sydney, Australia,volume 2, pp. 692–698. Morgan Kaufmann.Hartmann, C. R. P., and Rudolph, L. D. (1976) An optimumsymbol by symbol decoding rule for linear codes. IEEE Trans.Info. Theory IT-22: 514–517.Harvey, M., and Neal, R. M. (2000) Inference for belief networksusing coupling from the past. In Uncertainty in ArtificialIntelligence: Proc. Sixteenth Conf., pp. 256–263.Hebb, D. O. (1949) The Organization of Behavior. Wiley.Hendin, O., Horn, D., and Hopfield, J. J. (1994) Decompositionof a mixture of signals in a model of the olfactory bulb.Proc. Natl. Acad. Sci. USA 91 (13): 5942–5946.Hertz, J., Krogh, A., and Palmer, R. G. (1991) Introductionto the Theory of Neural Computation. Addison-Wesley.Hinton, G. (2001) Training products of experts by minimizingcontrastive divergence. Technical Report 2000-004, GatsbyComputational Neuroscience Unit, Univ. College London.Hinton, G., and Nowlan, S. (1987) How learning can guideevolution. Complex Systems 1: 495–502.Hinton, G. E., Dayan, P., Frey, B. J., and Neal, R. M.(1995) The wake-sleep algorithm for unsupervised neural networks.Science 268 (5214): 1158–1161.Hinton, G. E., and Ghahramani, Z. (1997) Generative modelsfor discovering sparse distributed representations. PhilosophicalTrans. Royal Society B.Hinton, G. E., and Sejnowski, T. J. (1986) Learning andrelearning in Boltzmann machines. In Parallel Distributed Processing,ed. by D. E. Rumelhart and J. E. McClelland, pp. 282–317. MIT Press.Hinton, G. E., and Teh, Y. W. (2001) Discovering multipleconstraints that are frequently approximately satisfied. InUncertainty in Artificial Intelligence: Proc. Seventeenth Conf.(UAI-2001), pp. 227–234. Morgan Kaufmann.Hinton, G. E., and van Camp, D. (1993) Keeping neuralnetworks simple by minimizing the description length of theweights. In Proc. 6th Annual Workshop on Comput. LearningTheory, pp. 5–13. ACM Press, New York, NY.Hinton, G. E., Welling, M., Teh, Y. W., and Osindero, S.(2001) A new view of ICA. In Proc. International Conf. onIndependent Component Analysis and Blind Signal Separation,volume 3.Hinton, G. E., and Zemel, R. S. (1994) Autoencoders, minimumdescription length and Helmholtz free energy. In Advancesin Neural Information Processing Systems 6 , ed. by J. D.Cowan, G. Tesauro, and J. Alspector. Morgan Kaufmann.Hodges, A. (1983) Alan Turing: The Enigma. Simon and Schuster.Hojen-Sorensen, P. A., Winther, O., and Hansen, L. K.(2002) Mean field approaches to independent component analysis.Neural Computation 14: 889–918.Holmes, C., and Denison, D. (2002) Perfect sampling forwavelet reconstruction of signals. IEEE Trans. Signal Processing50: 237–244.Holmes, C., and Mallick, B. (1998) Perfect simulation fororthogonal model mixing. Technical report, Imperial College,London.Hopfield, J. J. (1974) Kinetic proofreading: A new mechanismfor reducing errors in biosynthetic processes requiring highspecificity. Proc. Natl. Acad. Sci. USA 71 (10): 4135–4139.Hopfield, J. J. (1978) Origin of the genetic code: A testable hypothesisbased on tRNA structure, sequence, and kinetic proofreading.Proc. Natl. Acad. Sci. USA 75 (9): 4334–4338.Hopfield, J. J. (1980) The energy relay: A proofreading schemebased on dynamic cooperativity and lacking all characteristicsymptoms of kinetic proofreading in DNA replication and proteinsynthesis. Proc. Natl. Acad. Sci. USA 77 (9): 5248–5252.Hopfield, J. J. (1982) Neural networks and physical systemswith emergent collective computational abilities. Proc. Natl.Acad. Sci. USA 79: 2554–8.Hopfield, J. J. (1984) Neurons with graded response propertieshave collective computational properties like those of two-stateneurons. Proc. Natl. Acad. Sci. USA 81: 3088–92.Hopfield, J. J. (1987) Learning algorithms and probability distributionsin feed-forward and feed-back networks. Proc. Natl.Acad. Sci. USA 84: 8429–33.Hopfield, J. J., and Brody, C. D. (2000) What is a moment?“Cortical” sensory integration over a brief interval. Proc. Natl.Acad. Sci 97: 13919–13924.Hopfield, J. J., and Brody, C. D. (2001) What is a moment?Transient synchrony as a collective mechanism for spatiotemporalintegration. Proc. Natl. Acad. Sci 98: 1282–1287.Hopfield, J. J., and Tank, D. W. (1985) Neural computation ofdecisions in optimization problems. Biol. Cybernetics 52: 1–25.Howarth, P., and Bradley, A. (1986) The longitudinal aberrationof the human eye and its correction. Vision Res. 26: 361–366.Huber, M. (1998) Exact sampling and approximate countingtechniques. In Proc. 30th ACM Symposium on the Theory ofComputing, pp. 31–40.
Page 1 and 2:
Copyright Cambridge University Pres
Page 3 and 4:
Page 5 and 6:
Page 7 and 8:
Page 9 and 10:
Page 11 and 12:
Page 13 and 14:
Page 15 and 16:
Page 17 and 18:
Page 19 and 20:
Page 21 and 22:
Page 23 and 24:
Page 25 and 26:
Page 27 and 28:
Page 29 and 30:
Page 31 and 32:
Page 33 and 34:
Page 35 and 36:
Page 37 and 38:
Page 39 and 40:
Page 41 and 42:
Page 43 and 44:
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
Page 69 and 70:
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
Page 102 and 103:
Page 104 and 105:
Page 106 and 107:
Page 108 and 109:
¥¡¥¡¥¡¥¡¥¡¥¡¥¡¥¡¥
Page 110 and 111:
Page 112 and 113:
Page 114 and 115:
Page 116 and 117:
Page 118 and 119:
Page 120 and 121:
Page 122 and 123:
Page 124 and 125:
Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
Page 132 and 133:
Page 134 and 135:
Page 136 and 137:
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 150 and 151:
Page 152 and 153:
Page 154 and 155:
Page 156 and 157:
Page 158 and 159:
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
Page 166 and 167:
Page 168 and 169:
Page 170 and 171:
Page 172 and 173:
Page 174 and 175:
Page 176 and 177:
Page 178 and 179:
Page 180 and 181:
Page 182 and 183:
Page 184 and 185:
Page 186 and 187:
Page 188 and 189:
Page 190 and 191:
Page 192 and 193:
Page 194 and 195:
Page 196 and 197:
Page 198 and 199:
Page 200 and 201:
Page 202 and 203:
Page 204 and 205:
Page 206 and 207:
Page 208 and 209:
Page 210 and 211:
Page 212 and 213:
Page 214 and 215:
Page 216 and 217:
Page 218 and 219:
Page 220 and 221:
Page 222 and 223:
Page 224 and 225:
Page 226 and 227:
Page 228 and 229:
Page 230 and 231:
Page 232 and 233:
Page 234 and 235:
Page 236 and 237:
Page 238 and 239:
Page 240 and 241:
Page 242 and 243:
Page 244 and 245:
Page 246 and 247:
Page 248 and 249:
Page 250 and 251:
Page 252 and 253:
Page 254 and 255:
Page 256 and 257:
Page 258 and 259:
Page 260 and 261:
Page 262 and 263:
Page 264 and 265:
Page 266 and 267:
Page 268 and 269:
Page 270 and 271:
Page 272 and 273:
Page 274 and 275:
Page 276 and 277:
Page 278 and 279:
Page 280 and 281:
Page 282 and 283:
Page 284 and 285:
Page 286 and 287:
Page 288 and 289:
Page 290 and 291:
Page 292 and 293:
Page 294 and 295:
Page 296 and 297:
Page 298 and 299:
Page 300 and 301:
Page 302 and 303:
Page 304 and 305:
Page 306 and 307:
Page 308 and 309:
Page 310 and 311:
Page 312 and 313:
Page 314 and 315:
Page 316 and 317:
Page 318 and 319:
Page 320 and 321:
Page 322 and 323:
Page 324 and 325:
Page 326 and 327:
Page 328 and 329:
Page 330 and 331:
Page 332 and 333:
Page 334 and 335:
Page 336 and 337:
Page 338 and 339:
Page 340 and 341:
Page 342 and 343:
Page 344 and 345:
Page 346 and 347:
Page 348 and 349:
Page 350 and 351:
Page 352 and 353:
Page 354 and 355:
Page 356 and 357:
Page 358 and 359:
Page 360 and 361:
Page 362 and 363:
Page 364 and 365:
Page 366 and 367:
Page 368 and 369:
Page 370 and 371:
Page 372 and 373:
Page 374 and 375:
Page 376 and 377:
Page 378 and 379:
Page 380 and 381:
Page 382 and 383:
Page 384 and 385:
Page 386 and 387:
Page 388 and 389:
Page 390 and 391:
Page 392 and 393:
Page 394 and 395:
Page 396 and 397:
Page 398 and 399:
Page 400 and 401:
Page 402 and 403:
Page 404 and 405:
Page 406 and 407:
Page 408 and 409:
Page 410 and 411:
Page 412 and 413:
Page 414 and 415:
Page 416 and 417:
Page 418 and 419:
Page 420 and 421:
Page 422 and 423:
Page 424 and 425:
Page 426 and 427:
Page 428 and 429:
Page 430 and 431:
Page 432 and 433:
Page 434 and 435:
Page 436 and 437:
Page 438 and 439:
Page 440 and 441:
Page 442 and 443:
Page 444 and 445:
Page 446 and 447:
Page 448 and 449:
Page 450 and 451:
Page 452 and 453:
Page 454 and 455:
Page 456 and 457:
Page 458 and 459:
Page 460 and 461:
Page 462 and 463:
Page 464 and 465:
Page 466 and 467:
Page 468 and 469:
Page 470 and 471:
Page 472 and 473:
Page 474 and 475:
Page 476 and 477:
Page 478 and 479:
Page 480 and 481:
Page 482 and 483:
Page 484 and 485:
Page 486 and 487:
Page 488 and 489:
Page 490 and 491:
Page 492 and 493:
Page 494 and 495:
Page 496 and 497:
Page 498 and 499:
Page 500 and 501:
Page 502 and 503:
Page 504 and 505:
Page 506 and 507:
Page 508 and 509:
Page 510 and 511:
Page 512 and 513:
Page 514 and 515:
¡¤¢¢¤¨©¢£¡¢£¨¢£ ©Co
Page 516 and 517:
Page 518 and 519:
Page 520 and 521:
Page 522 and 523:
Page 524 and 525:
Page 526 and 527:
Page 528 and 529:
Page 530 and 531:
Page 532 and 533:
Page 534 and 535:
Page 536 and 537:
Page 538 and 539:
Page 540 and 541:
Page 542 and 543:
Page 544 and 545:
Page 546 and 547:
Page 548 and 549:
Page 550 and 551:
Page 552 and 553:
Page 554 and 555:
ttttCopyright Cambridge University
Page 556 and 557:
Page 558 and 559:
Page 560 and 561:
Page 562 and 563:
Page 564 and 565:
Page 566 and 567:
Page 568 and 569:
Page 570 and 571:
Page 572 and 573:
Page 574 and 575:
Page 576 and 577: Copyright Cambridge University Pres
Page 640: Copyright Cambridge University Pres
show all

Information Theory, Inference, and Learning ... - Inference Group

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?