• Source: Approximate inference

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


Laplace's approximation
Variational Bayesian methods
Markov chain Monte Carlo
Expectation propagation
Markov random fields
Bayesian networks
Variational message passing
Loopy and generalized belief propagation




See also


Statistical inference
Fuzzy logic
Data mining




References






External links


Tom Minka, Microsoft Research (Nov 2, 2009). "Machine Learning Summer School (MLSS), Cambridge 2009, Approximate Inference" (video lecture).

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