Information theory, inference, and learning algorithms:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2008
|
| Ausgabe: | 7. printing |
| Schlagworte: | |
| Online-Zugang: | kostenfrei Inhaltsverzeichnis Klappentext |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XII, 628 S. Ill., graph. Darst. |
| ISBN: | 9780521642989 |
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| 100 | 1 | |a MacKay, David J. C. |d 1967-2016 |e Verfasser |0 (DE-588)173311342 |4 aut | |
| 245 | 1 | 0 | |a Information theory, inference, and learning algorithms |c David J. C. MacKay |
| 250 | |a 7. printing | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2008 | |
| 300 | |a XII, 628 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
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Datensatz im Suchindex
| _version_ | 1804137672702164992 |
|---|---|
| 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
Marginalizai
ion
..................... 319
25
Exact Marginalization in Trellises
.............. 324
26
Exact Marginalization 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
Boltzmann 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
.............................
6I3
Index
................................. 620
Information
theory and inference, often taught separately, are
here united in one entertaining textbook. These topics lie atthe
heart of many exciting areas of contemporary science and engi¬
neering
-
communication, signal processing, data mining,
machine learning, pattern recognition,computational
neuro¬
science,
bioinformatics, and cryptography.
This textbook introduces theory in tandem with applications,
information theory is taught alongside practical communication
systems, such as arithmetic codingfor data compression and
sparse-graph codes for error-correction. A toolbox of inference
techniques, including message-passing algorithms, Monte
Carlo methods, and variational approximations, are developed
alongside applications of these tools to clustering, convolutional
codes, independent component analysis, and neural networks.
The
fi
nal
part of the book describes the state of the art in error-
correcting codes, including low-density parity-check codes,
turbo codes, and digital fountain codes
-
the twenty-first cen¬
tury standards for satellite communications, disk drives, and
data broadcast
Richly illustrated, filled with worked examples and over
400
exercises, some with detailed solutions, David
маска/ѕ
ground¬
breaking book is ideal for self-learning and for undergraduate or
graduate courses, interludes on crosswords, evolution, and sex
provide entertainment along the way.
in sum, this is a textbook on information, communication, and
codingfora new generation of students, and an unparalleled
entry point into these subjects for professionals in areas as
diverse as computational biology,financial engineering, and
machine learning.
This is an extraordinary and important
book, generous with insight and rich
with detail in statistics, information
theory, and probabilistic modeling across
a wide swathe of standard, creatively
original, and delightfully quirky topics.
David MacKayisan uncompromisingly
lucid thinker,from whom students,
faculty and practitioners all can learn.
Peter Dayan and Zoubin Ghahramani,
Gatsby computational Neurosdence Unit,
uniuersity College, London
An utterly original book that shows the
connections between such disparate
fields as information theory and coding,
inference, and statistical physics.
Dave Forney,
Massachusetts
institute
of Technology
An instant classic, covering everything
from shanrton sfundarnental theorems
to the postmodern theory of ldpc codes.
You ll wanttwo copies of this astonish¬
ing book, oneforthe office and one for
the fireside
л
home.
BobMcEliece,
California institute of Technology
|
| adam_txt |
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
Marginalizai
ion
. 319
25
Exact Marginalization in Trellises
. 324
26
Exact Marginalization 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
Boltzmann 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
.
6I3
Index
. 620
Information
theory and inference, often taught separately, are
here united in one entertaining textbook. These topics lie atthe
heart of many exciting areas of contemporary science and engi¬
neering
-
communication, signal processing, data mining,
machine learning, pattern recognition,computational
neuro¬
science,
bioinformatics, and cryptography.
This textbook introduces theory in tandem with applications,
information theory is taught alongside practical communication
systems, such as arithmetic codingfor data compression and
sparse-graph codes for error-correction. A toolbox of inference
techniques, including message-passing algorithms, Monte
Carlo methods, and variational approximations, are developed
alongside applications of these tools to clustering, convolutional
codes, independent component analysis, and neural networks.
The
fi
nal
part of the book describes the state of the art in error-
correcting codes, including low-density parity-check codes,
turbo codes, and digital fountain codes
-
the twenty-first cen¬
tury standards for satellite communications, disk drives, and
data broadcast
Richly illustrated, filled with worked examples and over
400
exercises, some with detailed solutions, David
маска/ѕ
ground¬
breaking book is ideal for self-learning and for undergraduate or
graduate courses, interludes on crosswords, evolution, and sex
provide entertainment along the way.
in sum, this is a textbook on information, communication, and
codingfora new generation of students, and an unparalleled
entry point into these subjects for professionals in areas as
diverse as computational biology,financial engineering, and
machine learning.
'This is an extraordinary and important
book, generous with insight and rich
with detail in statistics, information
theory, and probabilistic modeling across
a wide swathe of standard, creatively
original, and delightfully quirky topics.
David MacKayisan uncompromisingly
lucid thinker,from whom students,
faculty and practitioners all can learn.'
Peter Dayan and Zoubin Ghahramani,
Gatsby computational Neurosdence Unit,
uniuersity College, London
'An utterly original book that shows the
connections between such disparate
fields as information theory and coding,
inference, and statistical physics.'
Dave Forney,
Massachusetts
institute
of Technology
'An instant classic, covering everything
from shanrton'sfundarnental theorems
to the postmodern theory of ldpc codes.
You'll wanttwo copies of this astonish¬
ing book, oneforthe office and one for
the fireside
л
home."
BobMcEliece,
California institute of Technology |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | MacKay, David J. C. 1967-2016 |
| author_GND | (DE-588)173311342 |
| author_facet | MacKay, David J. C. 1967-2016 |
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| building | Verbundindex |
| bvnumber | BV023329777 |
| classification_rvk | AN 93000 ST 130 ST 300 |
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| ctrlnum | (OCoLC)315680647 (DE-599)BVBBV023329777 |
| dewey-full | 003.54 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 003 - Systems |
| dewey-raw | 003.54 |
| dewey-search | 003.54 |
| dewey-sort | 13.54 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Allgemeines Informatik |
| discipline_str_mv | Allgemeines Informatik |
| edition | 7. printing |
| format | Book |
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| id | DE-604.BV023329777 |
| illustrated | Illustrated |
| index_date | 2024-07-02T20:57:09Z |
| indexdate | 2024-07-09T21:16:01Z |
| institution | BVB |
| isbn | 9780521642989 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016513724 |
| oclc_num | 315680647 |
| open_access_boolean | 1 |
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| owner_facet | DE-355 DE-BY-UBR DE-91G DE-BY-TUM DE-19 DE-BY-UBM |
| physical | XII, 628 S. Ill., graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| spelling | MacKay, David J. C. 1967-2016 Verfasser (DE-588)173311342 aut Information theory, inference, and learning algorithms David J. C. MacKay 7. printing Cambridge [u.a.] Cambridge Univ. Press 2008 XII, 628 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Informationstheorie - Maschinelles Lernen - Inferenz <Künstliche Intelligenz> Inferenz Künstliche Intelligenz (DE-588)4333533-0 gnd rswk-swf Informationstheorie (DE-588)4026927-9 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 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.inference.phy.cam.ac.uk/mackay/itila/book.html kostenfrei Volltext Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016513724&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016513724&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | MacKay, David J. C. 1967-2016 Information theory, inference, and learning algorithms Informationstheorie - Maschinelles Lernen - Inferenz <Künstliche Intelligenz> Inferenz Künstliche Intelligenz (DE-588)4333533-0 gnd Informationstheorie (DE-588)4026927-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4333533-0 (DE-588)4026927-9 (DE-588)4193754-5 |
| 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_exact_search_txtP | 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 | Informationstheorie - Maschinelles Lernen - Inferenz <Künstliche Intelligenz> Inferenz Künstliche Intelligenz (DE-588)4333533-0 gnd Informationstheorie (DE-588)4026927-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Informationstheorie - Maschinelles Lernen - Inferenz <Künstliche Intelligenz> Inferenz Künstliche Intelligenz Informationstheorie Maschinelles Lernen |
| url | http://www.inference.phy.cam.ac.uk/mackay/itila/book.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016513724&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016513724&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT mackaydavidjc informationtheoryinferenceandlearningalgorithms |