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Information theory, inference, and learning algorithms:

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Bibliographic Details
Main Author:	MacKay, David J. C. 1967-2016 (Author)
Format:	Book
Language:	English
Published:	Cambridge, United Kingdom ; New York, NY, USA Cambridge University Press 2004
Edition:	Reprinted with corrections
Subjects:	Information theory Inferenz > Künstliche Intelligenz Maschinelles Lernen Informationstheorie
Links:	http://www.loc.gov/catdir/description/cam032/2003055133.html http://www.loc.gov/catdir/toc/cam031/2003055133.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=012954793&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Item Description:	Hier auch später erschienene, unveränderte Nachdrucke
Physical Description:	xii, 628 Seiten Illustrationen
ISBN:	0521642981 9780521642989

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Record in the Search Index

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adam_text	Contents Preface ............................. v 1 Introduction to Information Theory ............. 3 2 Probability, Entropy, and Inference .............. 22 3 More about Inference ..................... 48 I Data Compression ...................... 65 4 The Source Coding Theorem ................. 67 5 Symbol Codes ......................... 91 6 Stream Codes .......................... 110 7 Codes for Integers ....................... 132 II Noisy-Channel Coding .................... 137 8 Dependent Random Variables ................. 138 9 Communication over a Noisy Channel ............ 146 10 The Noisy-Channel Coding Theorem .............. 162 11 Error-Correcting Codes and Real Channels ......... 177 III Further Topics in Information Theory ............. 191 12 Hash Codes: Codes for Efficient Information Retrieval . . 193 13 Binary Codes ......................... 206 14 Very Good Linear Codes Exist ................ 229 15 Further Exercises on Information Theory .......... 233 16 Message Passing ........................ 241 17 Communication over Constrained Noiseless Channels . . . 248 18 Crosswords and Codebreaking ................ 260 19 Why have Sex? Information Acquisition and Evolution . . 269 IV Probabilities and Inference .................. 281 20 An Example Inference Task: Clustering ........... 284 21 Exact Inference by Complete Enumeration ......... 293 22 Maximum Likelihood and Clustering ............. 300 23 Useful Probability Distributions ............... 311 24 Exact MarginaHzation ..................... 319 25 Exact MarginaHzation in Trellises .............. 324 26 Exact MarginaHzation in Graphs ............... 334 27 Laplace s Method ....................... 341 28 Model Comparison and Occam s Razor ........... 343 29 Monte Carlo Methods ..................... 357 30 Efficient Monte Carlo Methods ................ 387 31 Ising Models .......................... 400 32 Exact Monte Carlo Sampling ................. 413 33 Variational Methods ...................... 422 34 Independent Component Analysis and Latent Variable Mod¬ elling .............................. 437 35 Random Inference Topics ................... 445 36 Decision Theory ........................ 451 37 Bayesian Inference and Sampling Theory .......... 457 V Neural networks ........................ 467 38 Introduction to Neural Networks ............... 468 39 The Single Neuron as a Classifier ............... 471 40 Capacity of a Single Neuron .................. 483 41 Learning as Inference ..................... 492 42 Hopfield Networks ....................... 505 43 Boitzmann Machines ...................... 522 44 Supervised Learning in Multilayer Networks ......... 527 45 Gaussian Processes ...................... 535 46 Deconvolution ......................... 549 VI Sparse Graph Codes ..................... 555 47 Low-Density Parity-Check Codes .............. 557 48 Convolutional Codes and Turbo Codes ............ 574 49 Repeat-Accumulate Codes .................. 582 50 Digital Fountain Codes .................... 589 VII Appendices .......................... 597 A Notation ............................ 598 В Some Physics .......................... 601 С Some Mathematics ....................... 605 Bibliography ............................. 613 Index ................................. 620
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spellingShingle	MacKay, David J. C. 1967-2016 Information theory, inference, and learning algorithms Information theory Inferenz Künstliche Intelligenz (DE-588)4333533-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Informationstheorie (DE-588)4026927-9 gnd
subject_GND	(DE-588)4333533-0 (DE-588)4193754-5 (DE-588)4026927-9
title	Information theory, inference, and learning algorithms
title_auth	Information theory, inference, and learning algorithms
title_exact_search	Information theory, inference, and learning algorithms
title_full	Information theory, inference, and learning algorithms David J.C. MacKay
title_fullStr	Information theory, inference, and learning algorithms David J.C. MacKay
title_full_unstemmed	Information theory, inference, and learning algorithms David J.C. MacKay
title_short	Information theory, inference, and learning algorithms
title_sort	information theory inference and learning algorithms
topic	Information theory Inferenz Künstliche Intelligenz (DE-588)4333533-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Informationstheorie (DE-588)4026927-9 gnd
topic_facet	Information theory Inferenz Künstliche Intelligenz Maschinelles Lernen Informationstheorie
url	http://www.loc.gov/catdir/description/cam032/2003055133.html http://www.loc.gov/catdir/toc/cam031/2003055133.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=012954793&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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