Approximate inference
From Infogalactic: the planetary knowledge core
Approximate inference methods make it possible to learn realistic models from big data by trading off computation time for accuracy, when exact learning and inference are computationally intractable.
Major methods classes
- Variational Bayesian methods
- Expectation propagation
- Markov random fields
- Bayesian networks
- loopy and generalized belief propagation
See also
References
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External links
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