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Topic	Contents
Directed Graphical Models	Comprehensive solutions for modeling causal relationships
Undirected Graphical Models	Expert guidance on modeling associations and dependencies
Markov Random Fields	Accurate solutions for probabilistic modeling
Conditional Random Fields	Specialized support for sequence labeling tasks
Factor Graph	Efficiently representing and solving complex systems
Bayesian Networks	In-depth analysis of probabilistic graphical model
Probabilistic Graphical Models	Understanding uncertainty and inference in complex systems
Graphical Models for Machine Learning	Tailored solutions for ML applications with graphical models

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