Graphical Models
A framework for modeling complex multivariate distributions using graphs, where nodes represent variables and edges represent probabilistic dependencies, widely used in AI and machine learning.
Implications
A type of probabilistic model that represents the conditional dependencies between random variables through a graph, often used in machine learning, statistics, and artificial intelligence to simplify the analysis of complex systems.
Example
Example: A healthcare analytics company uses graphical models to map out the relationships between different symptoms and diseases, improving diagnostic accuracy by understanding conditional dependencies.
Related Terms
Different from traditional statistical models, which might treat variables independently, graphical models explicitly represent dependencies, making them more suitable for complex, interconnected systems.
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