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Exploring the intricate relationships between variables, this work delves into probabilistic conditional independence structures, offering a comprehensive framework for understanding dependencies in complex systems. It presents theoretical foundations alongside practical applications, making it valuable for researchers and practitioners in statistics, machine learning, and related fields. The book emphasizes the significance of graphical models and provides insights into their implications for data analysis and inference, making it a crucial resource for advancing knowledge in probabilistic reasoning.
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Probabilistic Conditional Independence Structures, Milan Studeny
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- Pubblicato
- 2010
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