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
- Loopy and generalized belief propagation
gollark: I'm not the one buying this.
gollark: Seems fine.
gollark: As far as I know "80+ White" is below bronze is below silver is below gold is below platinum/titanium or something.
gollark: I mostly just go by the 80+ certification and how well-reviewed it is.
gollark: I see. It's not very efficient, though, compared to other ones.
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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