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.

## Contents

* 1 Major methods classes
* 2 See also
* 3 References
* 4 External links

## 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

[1][2]

## See also

* Statistical inference
* Fuzzy logic
* Data mining

## References

1. ↑ "Approximate Inference and Constrained Optimization". Uncertainty in Artificial Intelligence - UAI: 313–320. 2003. https://www.researchgate.net/publication/233871617.
2. ↑ "Approximate Inference". http://mlg.eng.cam.ac.uk/zoubin/approx.html. Retrieved 2013-07-15.

## External links

* Tom Minka, Microsoft Research (Nov 2, 2009). "Machine Learning Summer School (MLSS), Cambridge 2009, Approximate Inference". http://videolectures.net/mlss09uk_minka_ai/.

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