Information theory, inference, and learning algorithms:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, United Kingdom ; New York, NY, USA
Cambridge University Press
2004
|
| Ausgabe: | Reprinted with corrections |
| Schlagworte: | |
| Online-Zugang: | Publisher description Table of contents Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | xii, 628 Seiten Illustrationen |
| ISBN: | 0521642981 9780521642989 |
Internformat
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| 245 | 1 | 0 | |a Information theory, inference, and learning algorithms |c David J.C. MacKay |
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| 264 | 1 | |a Cambridge, United Kingdom ; New York, NY, USA |b Cambridge University Press |c 2004 | |
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Datensatz im Suchindex
| _version_ | 1804132991574736896 |
|---|---|
| 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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| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 003 - Systems |
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| discipline | Informatik Mathematik |
| edition | Reprinted with corrections |
| format | Book |
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| id | DE-604.BV019625527 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T20:01:37Z |
| institution | BVB |
| isbn | 0521642981 9780521642989 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-012954793 |
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| physical | xii, 628 Seiten Illustrationen |
| publishDate | 2004 |
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| publisher | Cambridge University Press |
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| spelling | MacKay, David J. C. 1967-2016 Verfasser (DE-588)173311342 aut Information theory, inference, and learning algorithms David J.C. MacKay Reprinted with corrections Cambridge, United Kingdom ; New York, NY, USA Cambridge University Press 2004 xii, 628 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Information theory Inferenz Künstliche Intelligenz (DE-588)4333533-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Informationstheorie (DE-588)4026927-9 gnd rswk-swf Informationstheorie (DE-588)4026927-9 s Inferenz Künstliche Intelligenz (DE-588)4333533-0 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 http://www.loc.gov/catdir/description/cam032/2003055133.html Publisher description http://www.loc.gov/catdir/toc/cam031/2003055133.html Table of contents Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf 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 Inhaltsverzeichnis |
| 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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