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

Variational Bayesian methods
Markov chain Monte Carlo
Expectation propagation
Markov random fields
Bayesian networks
- Variational message passing
Loopy and generalized belief propagation

See also

References

"Approximate Inference and Constrained Optimization". Uncertainty in Artificial Intelligence - UAI: 313–320. 2003.
"Approximate Inference". Retrieved 2013-07-15.

External links

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

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