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Kernel Papers

A joint distribution over \(N\) variables \(\underline{x}\) can permit complicated relationships between the variables. One general principle is that performing inference becomes easier in the absence of relationships between variables. This motivates us to specify relationships between variables as a graphs and then use the graph structure to design efficient inference algorithms.

- History
- Types of Graphical Models
- Parameter Estimation
- Maximum Likelihood

- Types of Inference Problems
- Marginalization
- Most Probable Configuration / MAP

- Exact Inference Algorithms
- Approximate Inference Algorithms
- Loopy Belief Propagation
- Variational Inference
- Graph Partitioning

- Learning
- Some variables unobserved, graph is known -> Expectation Maximization
- All variables observed, graph is unknown Chow Liu Algorithm
- Structure