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Causality, Correlation, and Artificial Intelligence for Rational Decision Making by Marwala, Tshilidzi is a nonfiction available to read on EtoBox.

What is Causality, Correlation, and Artificial Intelligence for Rational Decision Making about?

Causality has been a subject of study for a long time. Often causality is confused with correlation. Human intuition has evolved such that it has learned to identify causality through correlation. In this book, four main themes are considered and these are causality, correlation, artificial intelligence and decision making. A correlation machine is defined and built using multi-layer perceptron network, principal component analysis, Gaussian Mixture models, genetic algorithms, expectation maximization technique, simulated annealing and particle swarm optimization. Furthermore, a causal machine is defined and built using multi-layer perceptron, radial basis function, Bayesian statistics and Hybrid Monte Carlo methods. Both these machines are used to build a Granger non-linear causality model. In addition, the Neyman–Rubin, Pearl and Granger causal models are studied and are unified. The automatic relevance determination is also applied to extend Granger causality framework to the non-linear domain. The concept of rational decision making is studied, and the theory of flexibly-bounded rationality is used to extend the theory of bounded rationality within the principle of the indivisi

Who reads Causality, Correlation, and Artificial Intelligence for Rational Decision Making?

Author

Marwala, Tshilidzi

Publisher

World Scientific Publishing Co Pte Ltd

Published

2015

Language

EN

ISBN

9780780394902

Category

nonfiction

Subjects

Engineering, Science, Technology

Rating

4.25 / 5 (969 ratings)

Updated

2026-03-14