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Belief propagation, also known as sum–product message passing, is a message-passing algorithm for performing inference on graphical models, such as Bayesian networks and Markov random fields. It calculates the marginal distribution for each unobserved node (or variable), conditional on any observed nodes (or variables). Belief propagation is commonly…
Products, Gaussian belief propagation (GaBP) & Related algorithm and complexity issues
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algorithm propagation belief messages displaystyle message graphs factor graph node variable nodes one marginal shown tree set case convergence known
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Belief propagation | is a | variant of the belief propagation algorithm when the underlying distributions are Gaussian | 0.90 | text |
| Belief propagation | related to Approximate algorithm for general graphs | Although | 0.60 | section |
| Belief propagation | related to Approximate algorithm for general graphs | The | 0.60 | section |
| Belief propagation | related to Approximate algorithm for general graphs | Instead | 0.60 | section |
| Belief propagation | related to Approximate algorithm for general graphs | It | 0.60 | section |
| Belief propagation | related to Approximate algorithm for general graphs | Several | 0.60 | section |
| Belief propagation | related to Approximate algorithm for general graphs | There | 0.60 | section |
| Belief propagation | related to Approximate algorithm for general graphs | Techniques | 0.60 | section |
| Belief propagation | related to Approximate algorithm for general graphs | EXIT | 0.60 | section |
| Belief propagation | related to Description of the sum-product algorithm | Variants | 0.60 | section |
| Belief propagation | related to Description of the sum-product algorithm | Bayesian | 0.60 | section |
| Belief propagation | related to Description of the sum-product algorithm | Markov | 0.60 | section |
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