Causality, correlation and artificial intelligence for rational decision making:
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| Format: | Book |
| Language: | English |
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New Jersey ; London ; Singapore ; Beijing ; Shanghai ; Hong Kong ; Taipei ; Chennai
World Scientific
[2015]
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturangaben |
| Physical Description: | xiii, 192 Seiten Illustrationen, Diagramme |
| ISBN: | 9789814630863 |
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MARC
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Record in the Search Index
| _version_ | 1804153137097867264 |
|---|---|
| adam_text | Contents
Preface v
Acknowledgments
vii
1.
Introduction to Artificial Intelligence based Decision Making
1
1.1
Introduction
........................... 1
1.2
Correlation
............................ 2
1.2.1
What is correlation?
................... 2
1.2.2
Correlation function
................... 2
1.3
Causality
............................. 4
1.3.1
What is causality?
..................... 4
1.3.2
Theories of causality
................... 4
1.3.3
What is a causal function
?................ 10
1.3.4
How to detect causation?
................. 11
1.4
Introduction to Artificial Intelligence
..............
II
1.4.1
Neural networks
.....................
M
1.4.2
Hopfield networks
.................... 12
1.4.3
Genetic algorithm
..................... 13
1.4.4
Particle swarm optimization
............... 13
1.4.5
Simulated annealing
................... 14
1.5
Rational Decision Making
.................... 14
1.6
Summary and Outline of the Book
................ 15
1.7
Conclusions
........................... 16
References
............................... 16
2.
What is a Correlation Machine?
23
2.1
Introduction
........................... 23
2.2
Correlation Machines
...................... 24
2.2.1
Auto-associative memory network
............ 24
Contents
2.2.2 Principal
component
analysis
............... 26
2.2.3
Expectation maximization algorithm
........... 28
2.3
Genetic Algorithm
........................ 32
2.3.1
Initialization
....................... 33
2.3.2
Crossover
......................... 33
2.3.3
Mutation
......................... 34
2.3.4
Reproduction
....................... 34
2.3.5
Termination
........................ 35
2.4
Multi-layer Perceptron
...................... 35
2.5
Experimental Comparison
.................... 36
2.6
Conclusions
........................... 38
References
............................... 38
3.
What is a Causal Machine?
43
3.1
Introduction
........................... 43
3.2
Induction, Deduction, and Abduction
.............. 44
3.3
What is Causality?
........................ 45
3.4
Multi-layer Perceptron Causal Machine
............. 47
3.4.1
The architecture of the
MLP
causal machine
....... 48
3.4.2
Interstate conflict
..................... 50
3.5
Radial Basis Function Causal Machine
............. 51
3.5.1
Theoretical foundation
.................. 51
3.5.2
Applications to condition monitoring
........... 54
3.6
Fuzzy Inference System Causal Machine
............ 55
3.6.1
Theoretical foundation
.................. 55
3.6.2
Application to a steam generator
............. 58
3.7
Conclusions
........................... 59
References
............................... 59
4.
Correlation Machines Using Optimization Methods
65
4.1
Introduction
........................... 65
4.2
Multi-layer Perceptron Neural Network
............. 66
4.3
Missing Data Estimation Technique
............... 67
4.4
Genetic Algorithms
....................... 67
4.4.1
Initialization
....................... 68
4.4.2
Crossover
......................... 68
4.4.3
Mutation
......................... 69
4.4.4
Reproduction
....................... 69
4.4.5
Termination
........... 70
Contents xi
4.5
Partide
Swarm Optimization
.................. 70
4.6
Simulated Annealing
....................... 73
4.6.1
SA
parameters
...................... 75
4.6.2
Transition probabilities
.................. 75
4.6.3
Monte Carlo method
................... 75
4.6.4
Markov Chain Monte Carlo
............... 76
4.6.5
Acceptance probability function: Metropolis
algorithm
......................... 76
4.6.6
Cooling schedule
..................... 77
4.7
Missing Data Estimation: Case Studies
............. 77
4.7.1
Mechanical system
.................... 78
4.7.2
Modeling of beer tasting
................. 79
4.8
Conclusions
........................... 79
References
............................... 80
5.
Neural Networks for Modeling Granger Causality
87
5.1
Introduction
........................... 87
5.2
Granger Causality
........................ 88
5.3
Multi-layer Perceptron for Granger Causality
.......... 89
5.3.1
Bayesian statistics
.................... 90
5.3.2
Hybrid Monte Carlo (HMC)
............... 91
5.4
RB
F
for Granger Causality
................... 94
5.4.1
The Ar-means
....................... 96
5.4.2
Pseudo-inverse methods
................. 96
5.5
Example: Mackey-Glass System
................ 97
5.6
Conclusions
........................... 99
References
............................... 99
6.
Rubin, Pearl and Granger Causality Models: A Unified View
105
6.1
Introduction
........................... 105
6.2
Neyman-Rubin Causal Model
.................. 106
6.2.1
Missing data mechanism
................. 107
6.2.2
Missing data imputation methods
............ 108
6.3
Pearl Causality
.......................... 109
6.3.1
Directed acyclic graph
.................. 109
6.3.2
Associations between variables
.............. 110
6.3.3
¿/-separation
........................ 112
6.3.4
Back-door adjustment
.................. 113
6.3.5
Front-door adjustment
.................. 114
*ü Contents
6.3.6
Rules for do-calculus
...................
II5
6.3.7
Pearl inferred causation algorithm
............ 116
6.3.8
Examples of using do-calculus
.............. 117
6.4
Granger Causality
........................
I*8
6.5
Comparison: Neyman-Rubin, Pearl and Granger Causality
... 119
6.6
Conclusions
........................... 120
References
............................... 120
7.
Causal, Correlation and Automatic Relevance Determination
Machines for Granger Causality
125
7.1
Introduction
........................... 125
7.2
Causal Machine to Granger Causality
.............. 126
7.2.1
Multi-layer perceptron
.................. 128
7.2.2
Scaled conjugate gradient method
............ 129
7.3
Correlation Machine to Granger Causality
........... 131
7.3.1
Auto-associative network for missing data estimation
. . 131
7.3.2
Nelder-Mead simplex optimization method
....... 132
7.3.3
Granger causality
..................... 135
7.4
Automatic Relevance Determination for Granger Causality
. . . 136
7.5
Experimental Investigation: Mackey-Glass Time-Delay
Differential Equation
....................... 139
7.6
Conclusions
........................... 141
References
............................... 141
8.
Rexibly-bounded Rationality
147
8.1
Introduction
........................... 147
8.2
Rational Decision Making: A Causal Approach
......... 149
8.3
Rational Decision Making Process
............... 149
8.4
Bounded-Rational Decision Making
............... 150
8.5
Rexibly-bounded Rational Decision Making
.......... 152
8.5.1
Advanced information processing
............ 154
8.5.2
Missing data estimation
.................. 156
8.5.3
Intelligent machines
................... 156
8.6
Experimental Investigations
................... 157
8.6.1
Condition monitoring
................... 157
8.6.2 HIV
modeling
....................... 159
8.7
Conclusions
................ 160
References
....................
Contents
xiii
9.
Marginalization of Irrationality in Decision Making
167
9.1
Introduction
........................... 167
9.2
Rational Decision Making
.................... 168
9.3
What is Irrationality?
....................... 170
9.4
Marginalization of Irrationality Theory
............. 172
9.5
Irrational Decision Making and the Theory of Marginalization
of Irrationality in Decision Making
............... 173
9.6
Application of the Marginalization of Irrationality Theory
for Breast Cancer Diagnosis
................... 175
9.6.1 MLP............................ 176
9.6.2
RBF
............................ 178
9.6.3
Auto-associative neural network based on the
MLP ... 179
9.6.4
Auto-associative network based on the RBF
....... 180
9.7
Conclusions
........................... 182
References
............................... 182
10.
Conclusions and Further Work
187
10.1
Introduction
........................... 187
10.2
Way Forward
........................... 188
References
............................... 189
Index
191
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| spelling | Marwala, Tshilidzi 1971- Verfasser (DE-588)1038380928 aut Causality, correlation and artificial intelligence for rational decision making Tshilidzi Marwala, University of Johannesburg, South Africa New Jersey ; London ; Singapore ; Beijing ; Shanghai ; Hong Kong ; Taipei ; Chennai World Scientific [2015] © 2015 xiii, 192 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Literaturangaben Entscheidungstheorie (DE-588)4138606-1 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Entscheidungstheorie (DE-588)4138606-1 s DE-604 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027878558&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Marwala, Tshilidzi 1971- Causality, correlation and artificial intelligence for rational decision making Entscheidungstheorie (DE-588)4138606-1 gnd |
| subject_GND | (DE-588)4138606-1 (DE-588)4143413-4 |
| title | Causality, correlation and artificial intelligence for rational decision making |
| title_auth | Causality, correlation and artificial intelligence for rational decision making |
| title_exact_search | Causality, correlation and artificial intelligence for rational decision making |
| title_full | Causality, correlation and artificial intelligence for rational decision making Tshilidzi Marwala, University of Johannesburg, South Africa |
| title_fullStr | Causality, correlation and artificial intelligence for rational decision making Tshilidzi Marwala, University of Johannesburg, South Africa |
| title_full_unstemmed | Causality, correlation and artificial intelligence for rational decision making Tshilidzi Marwala, University of Johannesburg, South Africa |
| title_short | Causality, correlation and artificial intelligence for rational decision making |
| title_sort | causality correlation and artificial intelligence for rational decision making |
| topic | Entscheidungstheorie (DE-588)4138606-1 gnd |
| topic_facet | Entscheidungstheorie Aufsatzsammlung |
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