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
{{cite journal|url=https://www.researchgate.net/publication/233871617|title=Approximate Inference and Constrained Optimization|journal=Uncertainty in Artificial Intelligence |pages=313–320|year=2003}}{{cite web|url=http://mlg.eng.cam.ac.uk/zoubin/approx.html|title=Approximate Inference|accessdate=2013-07-15}}
See also
References
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External links
- {{cite web|url=http://videolectures.net/mlss09uk_minka_ai/|title=Machine Learning Summer School (MLSS), Cambridge 2009, Approximate Inference|author= Tom Minka, Microsoft Research|date=Nov 2, 2009|type=video lecture}}