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Causality, correlation, and artificial intelligence for rational decision making

Author: Tshilidzi Marwala
Publisher: New Jersey : World Scientific, [2015]
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
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Document Type: Book
All Authors / Contributors: Tshilidzi Marwala
ISBN: 9789814630863 9814630861
OCLC Number: 883836377
Description: xiii, 192 pages : illustrations ; 25 cm
Contents: Machine generated contents note: 1. Introduction to Artificial Intelligence based Decision Making --
1.1. Introduction --
1.2. Correlation --
1.2.1. What is correlation? --
1.2.2. Correlation function --
1.3. Causality --
1.3.1. What is causality? --
1.3.2. Theories of causality --
1.3.3. What is a causal function? --
1.3.4. How to detect causation? --
1.4. Introduction to Artificial Intelligence --
1.4.1. Neural networks --
1.4.2. Hopfield networks --
1.4.3. Genetic algorithm --
1.4.4. Particle swarm optimization --
1.4.5. Simulated annealing --
1.5. Rational Decision Making --
1.6. Summary and Outline of the Book --
1.7. Conclusions --
References --
2. What is a Correlation Machine? --
2.1. Introduction --
2.2. Correlation Machines --
2.2.1. Auto-associative memory network --
2.2.2. Principal component analysis --
2.2.3. Expectation maximization algorithm --
2.3. Genetic Algorithm --
2.3.1. Initialization --
2.3.2. Crossover --
2.3.3. Mutation --
2.3.4. Reproduction. 2.3.5. Termination --
2.4. Multi-layer Perceptron --
2.5. Experimental Comparison --
2.6. Conclusions --
References --
3. What is a Causal Machine? --
3.1. Introduction --
3.2. Induction, Deduction, and Abduction --
3.3. What is Causality? --
3.4. Multi-layer Perceptron Causal Machine --
3.4.1. The architecture of the MLP causal machine --
3.4.2. Interstate conflict --
3.5. Radial Basis Function Causal Machine --
3.5.1. Theoretical foundation --
3.5.2. Applications to condition monitoring --
3.6. Fuzzy Inference System Causal Machine --
3.6.1. Theoretical foundation --
3.6.2. Application to a steam generator --
3.7. Conclusions --
References --
4. Correlation Machines Using Optimization Methods --
4.1. Introduction --
4.2. Multi-layer Perceptron Neural Network --
4.3. Missing Data Estimation Technique --
4.4. Genetic Algorithms --
4.4.1. Initialization --
4.4.2. Crossover --
4.4.3. Mutation --
4.4.4. Reproduction --
4.4.5. Termination --
4.5. Particle Swarm Optimization --
4.6. Simulated Annealing --
4.6.1. SA parameters. 4.6.2. Transition probabilities --
4.6.3. Monte Carlo method --
4.6.4. Markov Chain Monte Carlo --
4.6.5. Acceptance probability function: Metropolis algorithm --
4.6.6. Cooling schedule --
4.7. Missing Data Estimation: Case Studies --
4.7.1. Mechanical system --
4.7.2. Modeling of beer tasting --
4.8. Conclusions --
References --
5. Neural Networks for Modeling Granger Causality --
5.1. Introduction --
5.2. Granger Causality --
5.3. Multi-layer Perceptron for Granger Causality --
5.3.1. Bayesian statistics --
5.3.2. Hybrid Monte Carlo (HMC) --
5.4. RBF for Granger Causality --
5.4.1. The k-means --
5.4.2. Pseudo-inverse methods --
5.5. Example: Mackey --
Glass System --
5.6. Conclusions --
References --
6. Rubin, Pearl and Granger Causality Models: A Unified View --
6.1. Introduction --
6.2. Neyman-Rubin Causal Model --
6.2.1. Missing data mechanism --
6.2.2. Missing data imputation methods --
6.3. Pearl Causality --
6.3.1. Directed acyclic graph --
6.3.2. Associations between variables --
6.3.3. d-separation. 6.3.4. Back-door adjustment --
6.3.5. Front-door adjustment --
6.3.6. Rules for do-calculus --
6.3.7. Pearl inferred causation algorithm --
6.3.8. Examples of using do-calculus --
6.4. Granger Causality --
6.5. Comparison: Neyman-Rubin, Pearl and Granger Causality --
6.6. Conclusions --
References --
7. Causal, Correlation and Automatic Relevance Determination Machines for Granger Causality --
7.1. Introduction --
7.2. Causal Machine to Granger Causality --
7.2.1. Multi-layer perceptron --
7.2.2. Scaled conjugate gradient method --
7.3. Correlation Machine to Granger Causality --
7.3.1. Auto-associative network for missing data estimation --
7.3.2. Nelder-Mead simplex optimization method --
7.3.3. Granger causality --
7.4. Automatic Relevance Determination for Granger Causality --
7.5. Experimental Investigation: Mackey-Glass Time-Delay Differential Equation --
7.6. Conclusions --
References --
8. Flexibly-bounded Rationality --
8.1. Introduction --
8.2. Rational Decision Making: A Causal Approach --
8.3. Rational Decision Making Process. 8.4. Bounded-Rational Decision Making --
8.5. Flexibly-bounded Rational Decision Making --
8.5.1. Advanced information processing --
8.5.2. Missing data estimation --
8.5.3. Intelligent machines --
8.6. Experimental Investigations --
8.6.1. Condition monitoring --
8.6.2. HIV modeling --
8.7. Conclusions --
References --
9. Marginalization of Irrationality in Decision Making --
9.1. Introduction --
9.2. Rational Decision Making --
9.3. What is Irrationality? --
9.4. Marginalization of Irrationality Theory --
9.5. Irrational Decision Making and the Theory of Marginalization of Irrationality in Decision Making --
9.6. Application of the Marginalization of Irrationality Theory for Breast Cancer Diagnosis --
9.6.1. MLP --
9.6.2. RBF --
9.6.3. Auto-associative neural network based on the MLP --
9.6.4. Auto-associative network based on the RBF --
9.7. Conclusions --
References --
10. Conclusions and Further Work --
10.1. Introduction --
10.2. Way Forward --
References.
Responsibility: Tshilidzi Marwala (University of Johannesburg, South Africa).

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