Bayesian nets are used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. This book brings together how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy.
The volume is composed of the invited and contributed papers presented at the Workshop on Mathematical Models for Handling Partial Knowledge in Artificial Intelligence, held at the Ettore Majorana Center for Scientific Culture of Erice ...
The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence .
2.12 Koster, J. T. A. (1996): Markov properties of nonrecursive causal models. Annals of Statistics, 24, 2148–2177. 2.13 Leamer, E. E. (1985): Vector autoregression for causal inference? CarnegieRochester Conference Series on Pubic ...
It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.
In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the...
In science, business, and policymaking -- anywhere data are used in prediction -- two sorts of problems requiring very different methods of analysis often arise. The first, problems of recognition...
From the basic concepts and nomenclature of causal modeling to decision tree analysis, qualitative methods, and quantitative modeling tools, this book offers a toolbox for MBA students and business professionals to make successful decisions ...
Proceedings, 1988. XIV,492 pages, 1989. Vol. 56: J.K. Lindsey, The Analysis of Categorical Data Using GLIM. V, 168 pages, 1989. Vol. 57: A. Decarli, B.J. Francis, R. Gilchrist, G.U.H. Seeber (Eds.), Statistical Modelling.
This is an open access book. This open access book assesses the potential of data-driven methods in industrial process monitoring engineering.
Causal inference is perhaps the most important form of reasoning in the sciences. A panoply of disciplines, ranging from epidemiology to biology, from econometrics to physics, make use of probability...