# Product of experts

**Product of experts** (PoE) is a machine learning technique. It models a probability distribution by combining the output from several simpler distributions.
It was proposed by Geoff Hinton, along with an algorithm for training the parameters of such a system.

The core idea is to combine several probability distributions ("experts") by multiplying their density functionsâ€”making the PoE classification similar to an "and" operation. This allows each expert to make decisions on the basis of a few dimensions without having to cover the full dimensionality of a problem.

This is related to (but quite different from) a mixture model, where several probability distributions are combined via an "or" operation, which is a weighted sum of their density functions.

## External links

- Hinton, Geoffrey E. (2002). "Training Products of Experts by Minimizing Contrastive Divergence" (PDF).
*Neural Computation*.**14**(8): 1771â€“1800. doi:10.1162/089976602760128018. PMID 12180402. Retrieved 2009-10-25.