Bayesian networks and decision graphs:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
Springer
2007
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | Information science and statistics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XVI, 447 S. graph. Darst. |
| ISBN: | 0387682813 9780387682815 |
Internformat
MARC
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| 100 | 1 | |a Jensen, Finn V. |d 1945- |e Verfasser |0 (DE-588)123202752 |4 aut | |
| 245 | 1 | 0 | |a Bayesian networks and decision graphs |c Finn V. Jensen ; Thomas D. Nielsen |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a New York [u.a.] |b Springer |c 2007 | |
| 300 | |a XVI, 447 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Information science and statistics | |
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 650 | 4 | |a Apprentissage automatique | |
| 650 | 7 | |a Inferência bayesiana (inferência estatística) |2 larpcal | |
| 650 | 4 | |a Prise de décision | |
| 650 | 7 | |a Redes neurais |2 larpcal | |
| 650 | 4 | |a Réseaux neuronaux (Informatique) | |
| 650 | 4 | |a Statistique bayésienne - Informatique | |
| 650 | 7 | |a Teoria da decisão |2 larpcal | |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Bayesian statistical decision theory |x Data processing | |
| 650 | 4 | |a Decision making | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Neural networks (Computer science) | |
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Datensatz im Suchindex
| _version_ | 1804137460681146368 |
|---|---|
| adam_text | Table
of
Contents
Preface
v
1
Prerequisites on Probability Theory
....................... 1
1.1
Two Perspectives on Probability Theory
.................... 1
1.2
Fundamentals of Probability Theory
....................... 2
1.2.1
Conditional Probabilities
........................... 4
1.2.2
Probability Calculus
............................... 5
1.2.3
Conditional Independence
.......................... 6
1.3
Probability Calculus for Variables
......................... 7
1.3.1
Calculations with Probability Tables: An Example
..... 11
1.4
An Algebra of Potentials
................................. 13
1.5
Random Variables
....................................... 15
1.5.1
Continuous Distributions
........................... 15
1.6
Exercises
............................................... 16
Part I Probabilistic Graphical Models
2
Causal and Bayesian Networks
............................ 23
2.1
Reasoning Under Uncertainty
............................. 23
2.1.1
Car Start Problem
................................. 23
2.1.2
A Causal Perspective on the Car Start Problem
....... 24
2.2
Causal Networks and d-Separation
......................... 26
2.2.1
d-separation
...................................... 30
2.3
Bayesian Networks
...................................... 32
2.3.1
Definition of Bayesian Networks
..................... 32
2.3.2
The Chain Rule for Bayesian Networks
............... 35
2.3.3
Inserting Evidence
................................. 39
2.3.4
Calculating Probabilities in Practice
................. 41
2.4
Graphical Models
-
Formal Languages for Model Specification
42
2.5
Summarv
............................................... 44
Table
of
Contents
2.6
Bibliographical
Notes
.................................... 45
2.7
Exercises
............................................... 45
Building
Models
........................................... 51
3.1
Catching the
Structure
................................... 51
3.1.1
Milk Test
........................................ 52
3.1.2
Cold or Angina?
.................................. 54
3.1.3
Insemination
...................................... 55
3.1.4
A Simplified Poker Game
........................... 57
3.1.5
Naive
Bayes
Models
............................... 58
3.1.6
Causality
......................................... 60
3.2
Determining the Conditional Probabilities
.................. 60
3.2.1
Milk Test
........................................ 60
3.2.2
Stud Farm
........................................ 62
3.2.3
Poker Game
...................................... 66
3.2.4
Transmission of Symbol Strings
..................... 68
3.2.5
Cold or Angina?
.................................. 71
3.2.6
Why Causal Networks?
............................ 72
3.3
Modeling Methods
....................................... 73
3.3.1
Undirected Relations
.............................. 73
3.3.2
Noisy-Or
......................................... 75
3.3.3
Divorcing
........................................ 78
3.3.4
Noisy Functional Dependence
....................... 80
3.3.5
Expert Disagreements
.............................. 81
3.3.6
Object-Oriented Bayesian Networks
.................. 84
3.3.7
Dynamic Bayesian Networks
........................ 91
3.3.8
How to Deal with Continuous Variables
.............. 93
3.3.9
Interventions
..................................... 96
3.4
Special Features
......................................... 97
3.4.1
Joint Probability Tables
............................ 98
3.4.2
Most-Probable Explanation
......................... 98
3.4.3
Data Conflict
..................................... 98
3.4.4
Sensitivity Analysis
................................ 99
3.5
Summary
...............................................100
3.6
Bibliographical Notes
....................................101
3.7
Exercises
...............................................102
Belief Updating in Bayesian Networks
.....................109
4.1
Introductory Examples
...................................109
4.1.1
A Single Marginal
.................................110
4.1.2
Different Evidence Scenarios
........................
Ill
4.1.3
All Marginals
.....................................114
4.2
Graph-Theoretic Representation
...........................115
4.2.1
Task and Notation
................................115
4.2.2
Domain Graphs
...................................116
Table
of Contents
xiii
4.3
Triangulated Graphs and Join Trees
.......................119
4.3.1
Join Trees
........................................122
4.4
Propagation in Junction Trees
............................124
4.4.1
Lazy Propagation in Junction Trees
.................127
4.5
Exploiting the Information Scenario
.......................130
4.5.1
Barren Nodes
.....................................130
4.5.2
d-Separation
......................................131
4.6
Nontriangulated Domain Graphs
..........................132
4.6.1 Triangulation
of Graphs
............................134
4.6.2 Triangulation
of Dynamic Bayesian Networks
.........137
4.7
Exact Propagation with Bounded Space
....................140
4.7.1
Recursive Conditioning
.............................140
4.8
Stochastic Simulation in Bayesian Networks
.................145
4.8.1
Probabilistic Logic Sampling
........................146
4.8.2
Likelihood Weighting
..............................148
4.8.3
Gibbs Sampling
...................................150
4.9
Loopy Belief Propagation
.................................152
4.10
Summary
...............................................154
4.11
Bibliographical Notes
....................................156
4.12
Exercises
...............................................157
Analysis Tools for Bayesian Networks
.....................167
5.1
IEJ Trees
...............................................168
5.2
Joint Probabilities and
Л
-Saturated Junction Trees
..........169
5.2.1
Л
-Saturated Junction Trees
.........................169
5.3
Configuration of Maximal Probability
......................171
5.4
Axioms for Propagation in Junction Trees
..................173
5.5
Data Conflict
...........................................174
5.5.1
Insemination
......................................175
5.5.2
The Conflict Measure conf
..........................175
5.5.3
Conflict or Rare Case
..............................176
5.5.4
Tracing of Conflicts
................................177
5.5.5
Other Approaches to Conflict Detection
..............179
5.6
SE
Analysis
.............................................179
5.6.1
Example and Definitions
...........................179
5.6.2
ft-Saturated Junction Trees and
SE
Analysis
..........182
5.7
Sensitivity to Parameters
.................................184
5.7.1
One-Way Sensitivity Analysis
.......................187
5.7.2
Two-Way Sensitivity Analysis
.......................188
5.8
Summary
...............................................188
5.9
Bibliographical Notes
....................................190
5.10
Exercises
...............................................191
Table
of
Contents
Parameter
Estimation
.....................................195
6.1
Complete Data
..........................................195
6.1.1
Maximum
Likelihood
Estimation
....................196
6.1.2
Bayesian Estimation
...............................197
6.2
Incomplete Data
........................................200
6.2.1
Approximate Parameter
Estimation: The EM Algorithm.201
6.2.2
*Why
We Cannot Perform
Exact
Parameter
Estimation
207
6.3
Adaptation
.............................................207
6.3.1
Fractional Updating
...............................210
6.3.2
Fading
...........................................211
6.3.3 *
Specification of an Initial Sample Size
...............212
6.3.4
Example: Strings of Symbols
........................213
6.3.5
Adaptation to Structure
............................214
6.3.6
*Fractional Updating as an Approximation
...........215
6.4
Tuning
.................................................218
6.4.1
Example
.........................................220
6.4.2
Determining
grad
dist(a:, y) as a Function of
t
........222
6.5
Summary
...............................................223
6.6
Bibliographical Notes
....................................225
6.7
Exercises
...............................................226
Learning the Structure of Bayesian Networks
..............229
7.1
Constraint-Based Learning Methods
.......................230
7.1.1
From Skeleton to DAG
.............................231
7.1.2
From Independence Tests to Skeleton
................234
7.1.3
Example
.........................................235
7.1.4
Constraint-Based Learning on Data Sets
.............237
7.2
Ockhanrs Razor
.........................................240
7.3
Score-Based Learning
....................................241
7.3.1
Score Functions
...................................242
7.3.2
Search Procedures
.................................245
7.3.3
Chow-Liu Trees
...................................250
7.3.4
*Bayesian Score Functions
..........................253
7.4
Summary
...............................................258
7.5
Bibliographical Notes
....................................260
7.6
Exercises
...............................................261
Bayesian Networks as Classifiers
...........................265
8.1
Naive
Bayes
Classifiers
...................................266
8.2
Evaluation of Classifiers
..................................268
8.3
Extensions of Naive
Bayes
Classifiers
.......................270
8.4
Classification Trees
......................................272
8.5
Summary
...............................................274
8.6
Bibliographical Notes
....................................275
8.7
Exercises
...............................................276
Table
of
Contents
Part
II Decision Graphs
9
Graphical Languages for Specification of Decision Problems279
9.1
One-Shot Decision Problems
..............................280
9.1.1
Fold or Call?
.....................................281
9.1.2
Mildew
...........................................282
9.1.3
One Decision in General
............................283
9.2
Utilities
................................................284
9.2.1
Instrumental Rationality
...........................287
9.3
Decision Trees
..........................................290
9.3.1
A Couple of Examples
.............................293
9.3.2
Coalesced Decision Trees
...........................295
9.3.3
Solving Decision Trees
.............................296
9.4
Influence Diagrams
......................................302
9.4.1
Extended Poker Model
.............................302
9.4.2
Definition of Influence Diagrams
....................305
9.4.3
Repetitive Decision Problems
.......................308
9.5
Asymmetric Decision Problems
............................310
9.5.1
Different Sources of Asymmetry
.....................314
9.5.2
Unconstrained Influence Diagrams
...................316
9.5.3
Sequential Influence Diagrams
......................322
9.6
Decision Problems with Unbounded Time Horizons
..........324
9.6.1
Markov Decision Processes
.........................324
9.6.2
Partially Observable Markov Decision Processes
.......330
9.7
Summary
...............................................332
9.8
Bibliographical Notes
....................................337
9.9
Exercises
...............................................337
10
Solution Methods for Decision Graphs
.....................343
10.1
Solutions to Influence Diagrams
...........................343
10.1.1
The Chain Rule for Influence Diagrams
..............345
10.1.2
Strategies and Expected Utilities
....................346
10.1.3
An Example
......................................352
10.2
Variable Elimination
.....................................353
10.2.1
Strong Junction Trees
..............................355
10.2.2
Required Past
....................................358
10.2.3
Policy Networks
...................................360
10.3
Node Removal and Arc Reversal
...........................362
10.3.1
Node Removal
....................................362
10.3.2
Arc Reversal
......................................363
10.3.3
An Example
......................................365
10.4
Solutions to Unconstrained Influence Diagrams
..............367
10.4.1
Minimizing the S-DAG
.............................367
10.4.2
Determining Policies and Step Functions
.............371
xvi
Table
of
Contents
10.5
Decision Problems Without a Temporal Ordering:
Troubleshooting
.........................................373
10.5.1
Action Sequences
..................................373
10.5.2
A Greedy Approach
...............................375
10.5.3
Call Service
.......................................378
10.5.4
Questions
........................................378
10.6
Solutions to Decision Problems with Unbounded Time Horizon
380
10.6.1
A Basic Solution
..................................380
10.6.2
Value Iteration
....................................381
10.6.3
Policy Iteration
...................................385
10.6.4
Solving Partially Observable Markov Decision Processes*388
10.7
Limited Memory Influence Diagrams
......................392
10.8
Summary
...............................................395
10.9
Bibliographical Notes
....................................400
lO.lOExercises
...............................................401
11
Methods for Analyzing Decision Problems
.................407
11.1
Value of Information
.....................................407
11.1.1
Test for Infected Milk?
.............................407
11.1.2
Myopic Hypothesis-Driven Data Request
.............409
11.1.3
Non-Utility-Based Value Functions
..................411
11.2
Finding the Relevant Past and Future of a Decision Problem
.. 413
11.2.1
Identifying the Required Past
.......................415
11.3
Sensitivity Analysis
......................................420
11.3.1
Example
.........................................421
11.3.2
One-Way Sensitivity Analysis in General
.............423
11.4
Summary
...............................................426
11.5
Bibliographical Notes
....................................427
11.6
Exercises
...............................................427
List of Notation
...............................................429
References
.....................................................431
Index
..........................................................441
|
| adam_txt |
Table
of
Contents
Preface
v
1
Prerequisites on Probability Theory
. 1
1.1
Two Perspectives on Probability Theory
. 1
1.2
Fundamentals of Probability Theory
. 2
1.2.1
Conditional Probabilities
. 4
1.2.2
Probability Calculus
. 5
1.2.3
Conditional Independence
. 6
1.3
Probability Calculus for Variables
. 7
1.3.1
Calculations with Probability Tables: An Example
. 11
1.4
An Algebra of Potentials
. 13
1.5
Random Variables
. 15
1.5.1
Continuous Distributions
. 15
1.6
Exercises
. 16
Part I Probabilistic Graphical Models
2
Causal and Bayesian Networks
. 23
2.1
Reasoning Under Uncertainty
. 23
2.1.1
Car Start Problem
. 23
2.1.2
A Causal Perspective on the Car Start Problem
. 24
2.2
Causal Networks and d-Separation
. 26
2.2.1
d-separation
. 30
2.3
Bayesian Networks
. 32
2.3.1
Definition of Bayesian Networks
. 32
2.3.2
The Chain Rule for Bayesian Networks
. 35
2.3.3
Inserting Evidence
. 39
2.3.4
Calculating Probabilities in Practice
. 41
2.4
Graphical Models
-
Formal Languages for Model Specification
42
2.5
Summarv
. 44
Table
of
Contents
2.6
Bibliographical
Notes
. 45
2.7
Exercises
. 45
Building
Models
. 51
3.1
Catching the
Structure
. 51
3.1.1
Milk Test
. 52
3.1.2
Cold or Angina?
. 54
3.1.3
Insemination
. 55
3.1.4
A Simplified Poker Game
. 57
3.1.5
Naive
Bayes
Models
. 58
3.1.6
Causality
. 60
3.2
Determining the Conditional Probabilities
. 60
3.2.1
Milk Test
. 60
3.2.2
Stud Farm
. 62
3.2.3
Poker Game
. 66
3.2.4
Transmission of Symbol Strings
. 68
3.2.5
Cold or Angina?
. 71
3.2.6
Why Causal Networks?
. 72
3.3
Modeling Methods
. 73
3.3.1
Undirected Relations
. 73
3.3.2
Noisy-Or
. 75
3.3.3
Divorcing
. 78
3.3.4
Noisy Functional Dependence
. 80
3.3.5
Expert Disagreements
. 81
3.3.6
Object-Oriented Bayesian Networks
. 84
3.3.7
Dynamic Bayesian Networks
. 91
3.3.8
How to Deal with Continuous Variables
. 93
3.3.9
Interventions
. 96
3.4
Special Features
. 97
3.4.1
Joint Probability Tables
. 98
3.4.2
Most-Probable Explanation
. 98
3.4.3
Data Conflict
. 98
3.4.4
Sensitivity Analysis
. 99
3.5
Summary
.100
3.6
Bibliographical Notes
.101
3.7
Exercises
.102
Belief Updating in Bayesian Networks
.109
4.1
Introductory Examples
.109
4.1.1
A Single Marginal
.110
4.1.2
Different Evidence Scenarios
.
Ill
4.1.3
All Marginals
.114
4.2
Graph-Theoretic Representation
.115
4.2.1
Task and Notation
.115
4.2.2
Domain Graphs
.116
Table
of Contents
xiii
4.3
Triangulated Graphs and Join Trees
.119
4.3.1
Join Trees
.122
4.4
Propagation in Junction Trees
.124
4.4.1
Lazy Propagation in Junction Trees
.127
4.5
Exploiting the Information Scenario
.130
4.5.1
Barren Nodes
.130
4.5.2
d-Separation
.131
4.6
Nontriangulated Domain Graphs
.132
4.6.1 Triangulation
of Graphs
.134
4.6.2 Triangulation
of Dynamic Bayesian Networks
.137
4.7
Exact Propagation with Bounded Space
.140
4.7.1
Recursive Conditioning
.140
4.8
Stochastic Simulation in Bayesian Networks
.145
4.8.1
Probabilistic Logic Sampling
.146
4.8.2
Likelihood Weighting
.148
4.8.3
Gibbs Sampling
.150
4.9
Loopy Belief Propagation
.152
4.10
Summary
.154
4.11
Bibliographical Notes
.156
4.12
Exercises
.157
Analysis Tools for Bayesian Networks
.167
5.1
IEJ Trees
.168
5.2
Joint Probabilities and
Л
-Saturated Junction Trees
.169
5.2.1
Л
-Saturated Junction Trees
.169
5.3
Configuration of Maximal Probability
.171
5.4
Axioms for Propagation in Junction Trees
.173
5.5
Data Conflict
.174
5.5.1
Insemination
.175
5.5.2
The Conflict Measure conf
.175
5.5.3
Conflict or Rare Case
.176
5.5.4
Tracing of Conflicts
.177
5.5.5
Other Approaches to Conflict Detection
.179
5.6
SE
Analysis
.179
5.6.1
Example and Definitions
.179
5.6.2
ft-Saturated Junction Trees and
SE
Analysis
.182
5.7
Sensitivity to Parameters
.184
5.7.1
One-Way Sensitivity Analysis
.187
5.7.2
Two-Way Sensitivity Analysis
.188
5.8
Summary
.188
5.9
Bibliographical Notes
.190
5.10
Exercises
.191
Table
of
Contents
Parameter
Estimation
.195
6.1
Complete Data
.195
6.1.1
Maximum
Likelihood
Estimation
.196
6.1.2
Bayesian Estimation
.197
6.2
Incomplete Data
.200
6.2.1
Approximate Parameter
Estimation: The EM Algorithm.201
6.2.2
*Why
We Cannot Perform
Exact
Parameter
Estimation
207
6.3
Adaptation
.207
6.3.1
Fractional Updating
.210
6.3.2
Fading
.211
6.3.3 *
Specification of an Initial Sample Size
.212
6.3.4
Example: Strings of Symbols
.213
6.3.5
Adaptation to Structure
.214
6.3.6
*Fractional Updating as an Approximation
.215
6.4
Tuning
.218
6.4.1
Example
.220
6.4.2
Determining
grad
dist(a:, y) as a Function of
t
.222
6.5
Summary
.223
6.6
Bibliographical Notes
.225
6.7
Exercises
.226
Learning the Structure of Bayesian Networks
.229
7.1
Constraint-Based Learning Methods
.230
7.1.1
From Skeleton to DAG
.231
7.1.2
From Independence Tests to Skeleton
.234
7.1.3
Example
.235
7.1.4
Constraint-Based Learning on Data Sets
.237
7.2
Ockhanrs Razor
.240
7.3
Score-Based Learning
.241
7.3.1
Score Functions
.242
7.3.2
Search Procedures
.245
7.3.3
Chow-Liu Trees
.250
7.3.4
*Bayesian Score Functions
.253
7.4
Summary
.258
7.5
Bibliographical Notes
.260
7.6
Exercises
.261
Bayesian Networks as Classifiers
.265
8.1
Naive
Bayes
Classifiers
.266
8.2
Evaluation of Classifiers
.268
8.3
Extensions of Naive
Bayes
Classifiers
.270
8.4
Classification Trees
.272
8.5
Summary
.274
8.6
Bibliographical Notes
.275
8.7
Exercises
.276
Table
of
Contents
Part
II Decision Graphs
9
Graphical Languages for Specification of Decision Problems279
9.1
One-Shot Decision Problems
.280
9.1.1
Fold or Call?
.281
9.1.2
Mildew
.282
9.1.3
One Decision in General
.283
9.2
Utilities
.284
9.2.1
Instrumental Rationality
.287
9.3
Decision Trees
.290
9.3.1
A Couple of Examples
.293
9.3.2
Coalesced Decision Trees
.295
9.3.3
Solving Decision Trees
.296
9.4
Influence Diagrams
.302
9.4.1
Extended Poker Model
.302
9.4.2
Definition of Influence Diagrams
.305
9.4.3
Repetitive Decision Problems
.308
9.5
Asymmetric Decision Problems
.310
9.5.1
Different Sources of Asymmetry
.314
9.5.2
Unconstrained Influence Diagrams
.316
9.5.3
Sequential Influence Diagrams
.322
9.6
Decision Problems with Unbounded Time Horizons
.324
9.6.1
Markov Decision Processes
.324
9.6.2
Partially Observable Markov Decision Processes
.330
9.7
Summary
.332
9.8
Bibliographical Notes
.337
9.9
Exercises
.337
10
Solution Methods for Decision Graphs
.343
10.1
Solutions to Influence Diagrams
.343
10.1.1
The Chain Rule for Influence Diagrams
.345
10.1.2
Strategies and Expected Utilities
.346
10.1.3
An Example
.352
10.2
Variable Elimination
.353
10.2.1
Strong Junction Trees
.355
10.2.2
Required Past
.358
10.2.3
Policy Networks
.360
10.3
Node Removal and Arc Reversal
.362
10.3.1
Node Removal
.362
10.3.2
Arc Reversal
.363
10.3.3
An Example
.365
10.4
Solutions to Unconstrained Influence Diagrams
.367
10.4.1
Minimizing the S-DAG
.367
10.4.2
Determining Policies and Step Functions
.371
xvi
Table
of
Contents
10.5
Decision Problems Without a Temporal Ordering:
Troubleshooting
.373
10.5.1
Action Sequences
.373
10.5.2
A Greedy Approach
.375
10.5.3
Call Service
.378
10.5.4
Questions
.378
10.6
Solutions to Decision Problems with Unbounded Time Horizon
380
10.6.1
A Basic Solution
.380
10.6.2
Value Iteration
.381
10.6.3
Policy Iteration
.385
10.6.4
Solving Partially Observable Markov Decision Processes*388
10.7
Limited Memory Influence Diagrams
.392
10.8
Summary
.395
10.9
Bibliographical Notes
.400
lO.lOExercises
.401
11
Methods for Analyzing Decision Problems
.407
11.1
Value of Information
.407
11.1.1
Test for Infected Milk?
.407
11.1.2
Myopic Hypothesis-Driven Data Request
.409
11.1.3
Non-Utility-Based Value Functions
.411
11.2
Finding the Relevant Past and Future of a Decision Problem
. 413
11.2.1
Identifying the Required Past
.415
11.3
Sensitivity Analysis
.420
11.3.1
Example
.421
11.3.2
One-Way Sensitivity Analysis in General
.423
11.4
Summary
.426
11.5
Bibliographical Notes
.427
11.6
Exercises
.427
List of Notation
.429
References
.431
Index
.441 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Jensen, Finn V. 1945- Nielsen, Thomas D. |
| author_GND | (DE-588)123202752 |
| author_facet | Jensen, Finn V. 1945- Nielsen, Thomas D. |
| author_role | aut aut |
| author_sort | Jensen, Finn V. 1945- |
| author_variant | f v j fv fvj t d n td tdn |
| building | Verbundindex |
| bvnumber | BV023189489 |
| callnumber-first | Q - Science |
| callnumber-label | QA279 |
| callnumber-raw | QA279.5 |
| callnumber-search | QA279.5 |
| callnumber-sort | QA 3279.5 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | SK 830 ST 285 ST 300 ST 301 |
| classification_tum | MAT 624f DAT 717f |
| ctrlnum | (OCoLC)141385221 (DE-599)BVBBV023189489 |
| dewey-full | 519.5/42 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/42 |
| dewey-search | 519.5/42 |
| dewey-sort | 3519.5 242 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik |
| discipline_str_mv | Informatik Mathematik |
| edition | 2. ed. |
| format | Book |
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| genre_facet | Lehrbuch |
| id | DE-604.BV023189489 |
| illustrated | Illustrated |
| index_date | 2024-07-02T20:04:15Z |
| indexdate | 2024-07-09T21:12:39Z |
| institution | BVB |
| isbn | 0387682813 9780387682815 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016375909 |
| oclc_num | 141385221 |
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| physical | XVI, 447 S. graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Springer |
| record_format | marc |
| series2 | Information science and statistics |
| spelling | Jensen, Finn V. 1945- Verfasser (DE-588)123202752 aut Bayesian networks and decision graphs Finn V. Jensen ; Thomas D. Nielsen 2. ed. New York [u.a.] Springer 2007 XVI, 447 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Information science and statistics Hier auch später erschienene, unveränderte Nachdrucke Apprentissage automatique Inferência bayesiana (inferência estatística) larpcal Prise de décision Redes neurais larpcal Réseaux neuronaux (Informatique) Statistique bayésienne - Informatique Teoria da decisão larpcal Datenverarbeitung Bayesian statistical decision theory Data processing Decision making Machine learning Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Entscheidungsgraph (DE-588)4362839-4 gnd rswk-swf Bayes-Netz (DE-588)4567228-3 gnd rswk-swf 1\p (DE-588)4123623-3 Lehrbuch gnd-content Bayes-Entscheidungstheorie (DE-588)4144220-9 s Neuronales Netz (DE-588)4226127-2 s Entscheidungsgraph (DE-588)4362839-4 s DE-604 Bayes-Netz (DE-588)4567228-3 s 2\p DE-604 Nielsen, Thomas D. Verfasser aut Erscheint auch als Online-Ausgabe 0-387-68282-1 Erscheint auch als Online-Ausgabe 978-0-387-68282-2 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016375909&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Jensen, Finn V. 1945- Nielsen, Thomas D. Bayesian networks and decision graphs Apprentissage automatique Inferência bayesiana (inferência estatística) larpcal Prise de décision Redes neurais larpcal Réseaux neuronaux (Informatique) Statistique bayésienne - Informatique Teoria da decisão larpcal Datenverarbeitung Bayesian statistical decision theory Data processing Decision making Machine learning Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Entscheidungsgraph (DE-588)4362839-4 gnd Bayes-Netz (DE-588)4567228-3 gnd |
| subject_GND | (DE-588)4226127-2 (DE-588)4144220-9 (DE-588)4362839-4 (DE-588)4567228-3 (DE-588)4123623-3 |
| title | Bayesian networks and decision graphs |
| title_auth | Bayesian networks and decision graphs |
| title_exact_search | Bayesian networks and decision graphs |
| title_exact_search_txtP | Bayesian networks and decision graphs |
| title_full | Bayesian networks and decision graphs Finn V. Jensen ; Thomas D. Nielsen |
| title_fullStr | Bayesian networks and decision graphs Finn V. Jensen ; Thomas D. Nielsen |
| title_full_unstemmed | Bayesian networks and decision graphs Finn V. Jensen ; Thomas D. Nielsen |
| title_short | Bayesian networks and decision graphs |
| title_sort | bayesian networks and decision graphs |
| topic | Apprentissage automatique Inferência bayesiana (inferência estatística) larpcal Prise de décision Redes neurais larpcal Réseaux neuronaux (Informatique) Statistique bayésienne - Informatique Teoria da decisão larpcal Datenverarbeitung Bayesian statistical decision theory Data processing Decision making Machine learning Neural networks (Computer science) Neuronales Netz (DE-588)4226127-2 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Entscheidungsgraph (DE-588)4362839-4 gnd Bayes-Netz (DE-588)4567228-3 gnd |
| topic_facet | Apprentissage automatique Inferência bayesiana (inferência estatística) Prise de décision Redes neurais Réseaux neuronaux (Informatique) Statistique bayésienne - Informatique Teoria da decisão Datenverarbeitung Bayesian statistical decision theory Data processing Decision making Machine learning Neural networks (Computer science) Neuronales Netz Bayes-Entscheidungstheorie Entscheidungsgraph Bayes-Netz Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016375909&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT jensenfinnv bayesiannetworksanddecisiongraphs AT nielsenthomasd bayesiannetworksanddecisiongraphs |