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

• Major methods classes
• See also
• References
• 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

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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).