Titlebook: Probabilistic Graphical Models; Principles and Appli Luis Enrique Sucar Textbook 2021Latest edition Springer Nature Switzerland AG 2021 Bay

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Graphical Causal Modelsortance. Then causal Bayesian networks are described, including two types of causal reasoning, prediction and counterfactuals. It introduces the . and . criteria, to take into account covariates that can affect causal inference. The chapter concludes with two examples of applications of causal model

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Causal Discovery Then the types of graphs used to represent partial models are introduced. Several algorithms for causal discovery from observational data are explained, including score-based and constraint-based; as well as a technique for linear models. Finally, it includes an example of learning causal models fr

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Luis Enrique Sucarementary and interrelated perspectives, at economy and firm-This book presents peer-reviewed, state-of-the-art conceptual and empirical papers devoted to changes in the international competitive position of the Central and Eastern European (CEE) region, its countries and businesses. While the unprec

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Luis Enrique Sucarand food security patterns over 25 years.Includes nine policThe book combines food security and agricultural competitiveness issues and treat them together. It starts with definitions and evolution of both concepts, followed by reviews on global and regional food security challenges. The book identi

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Luis Enrique Sucarade patterns and trends at various levels. We then, devise five categories of countries according to their net agricultural and food trade position and income level. The chapter ends with an analysis of reasons behind recent changes in global trade of agricultural products. The chapter suggests that